Changelog#
Unreleased#
What’s new?#
Incompatible changes#
None
v1.8.0 (2024-04-03)#
Thanks to our contributors#
We would like to give our special thanks to all the contributors who made the new version of Flower possible (in git shortlog
order):
Adam Narozniak
, Charles Beauville
, Daniel J. Beutel
, Daniel Nata Nugraha
, Danny
, Gustavo Bertoli
, Heng Pan
, Ikko Eltociear Ashimine
, Jack Cook
, Javier
, Raj Parekh
, Robert Steiner
, Sebastian van der Voort
, Taner Topal
, Yan Gao
, mohammadnaseri
, tabdar-khan
What’s new?#
Introduce Flower Next high-level API (stable) (#3002, #2934, #2958, #3173, #3174, #2923, #2691, #3079, #2961, #2924, #3166, #3031, #3057, #3000, #3113, #2957, #3183, #3180, #3035, #3189, #3185, #3190, #3191, #3195, #3197)
The Flower Next high-level API is stable! Flower Next is the future of Flower - all new features (like Flower Mods) will be built on top of it. You can start to migrate your existing projects to Flower Next by using
ServerApp
andClientApp
(check outquickstart-pytorch
orquickstart-tensorflow
, a detailed migration guide will follow shortly). Flower Next allows you to run multiple projects concurrently (we call this multi-run) and execute the same project in either simulation environments or deployment environments without having to change a single line of code. The best part? It’s fully compatible with existing Flower projects that useStrategy
,NumPyClient
& co.Introduce Flower Next low-level API (preview) (#3062, #3034, #3069)
In addition to the Flower Next high-level API that uses
Strategy
,NumPyClient
& co, Flower 1.8 also comes with a preview version of the new Flower Next low-level API. The low-level API allows for granular control of every aspect of the learning process by sending/receiving individual messages to/from client nodes. The newServerApp
supports registering a custommain
function that allows writing custom training loops for methods like async FL, cyclic training, or federated analytics. The newClientApp
supports registeringtrain
,evaluate
andquery
functions that can access the raw message received from theServerApp
. New abstractions likeRecordSet
,Message
andContext
further enable sending multiple models, multiple sets of config values and metrics, stateful computations on the client node and implementations of custom SMPC protocols, to name just a few.Introduce Flower Mods (preview) (#3054, #2911, #3083)
Flower Modifiers (we call them Mods) can intercept messages and analyze, edit or handle them directly. Mods can be used to develop pluggable modules that work across different projects. Flower 1.8 already includes mods to log the size of a message, the number of parameters sent over the network, differential privacy with fixed clipping and adaptive clipping, local differential privacy and secure aggregation protocols SecAgg and SecAgg+. The Flower Mods API is released as a preview, but researchers can already use it to experiment with arbirtrary SMPC protocols.
Fine-tune LLMs with LLM FlowerTune (#3029, #3089, #3092, #3100, #3114, #3162, #3172)
We are introducing LLM FlowerTune, an introductory example that demonstrates federated LLM fine-tuning of pre-trained Llama2 models on the Alpaca-GPT4 dataset. The example is built to be easily adapted to use different models and/or datasets. Read our blog post LLM FlowerTune: Federated LLM Fine-tuning with Flower for more details.
Introduce built-in Differential Privacy (preview) (#2798, #2959, #3038, #3147, #2909, #2893, #2892, #3039, #3074)
Built-in Differential Privacy is here! Flower supports both central and local differential privacy (DP). Central DP can be configured with either fixed or adaptive clipping. The clipping can happen either on the server-side or the client-side. Local DP does both clipping and noising on the client-side. A new documentation page explains Differential Privacy approaches and a new how-to guide describes how to use the new Differential Privacy components in Flower.
Introduce built-in Secure Aggregation (preview) (#3120, #3110, #3108)
Built-in Secure Aggregation is here! Flower now supports different secure aggregation protocols out-of-the-box. The best part? You can add secure aggregation to your Flower projects with only a few lines of code. In this initial release, we inlcude support for SecAgg and SecAgg+, but more protocols will be implemented shortly. We’ll also add detailed docs that explain secure aggregation and how to use it in Flower. You can already check out the new code example that shows how to use Flower to easily combine Federated Learning, Differential Privacy and Secure Aggregation in the same project.
Introduce
flwr
CLI (preview) (#2942, #3055, #3111, #3130, #3136, #3094, #3059, #3049, #3142)A new
flwr
CLI command allows creating new Flower projects (flwr new
) and then running them using the Simulation Engine (flwr run
).Introduce Flower Next Simulation Engine (#3024, #3061, #2997, #2783, #3184, #3075, #3047, #2998, #3009, #3008)
The Flower Simulation Engine can now run Flower Next projects. For notebook environments, there’s also a new
run_simulation
function that can runServerApp
andClientApp
.Handle SuperNode connection errors (#2969)
A SuperNode will now try to reconnect indefinitely to the SuperLink in case of connection errors. The arguments
--max-retries
and--max-wait-time
can now be passed to theflower-client-app
command.--max-retries
will define the number of tentatives the client should make before it gives up trying to reconnect to the SuperLink, and,--max-wait-time
defines the time before the SuperNode gives up trying to reconnect to the SuperLink.General updates to Flower Baselines (#2904, #2482, #2985, #2968)
There’s a new FedStar baseline. Several other baselined have been updated as well.
Improve documentation and translations (#3050, #3044, #3043, #2986, #3041, #3046, #3042, #2978, #2952, #3167, #2953, #3045, #2654, #3082, #2990, #2989)
As usual, we merged many smaller and larger improvements to the documentation. A special thank you goes to Sebastian van der Voort for landing a big documentation PR!
General updates to Flower Examples (3134, 2996, 2930, 2967, 2467, 2910, #2918, #2773, #3063, #3116, #3117)
Two new examples show federated training of a Vision Transformer (ViT) and federated learning in a medical context using the popular MONAI library.
quickstart-pytorch
andquickstart-tensorflow
demonstrate the new Flower NextServerApp
andClientApp
. Many other examples received considerable updates as well.General improvements (#3171, 3099, 3003, 3145, 3017, 3085, 3012, 3119, 2991, 2970, 2980, 3086, 2932, 2928, 2941, 2933, 3181, 2973, 2992, 2915, 3040, 3022, 3032, 2902, 2931, 3005, 3132, 3115, 2944, 3064, 3106, 2974, 3178, 2993, 3186, 3091, 3125, 3093, 3013, 3033, 3133, 3068, 2916, 2975, 2984, 2846, 3077, 3143, 2921, 3101, 2927, 2995, 2972, 2912, 3065, 3028, 2922, 2982, 2914, 3179, 3080, 2994, 3187, 2926, 3018, 3144, 3011, #3152, #2836, #2929, #2943, #2955, #2954)
Incompatible changes#
None
v1.7.0 (2024-02-05)#
Thanks to our contributors#
We would like to give our special thanks to all the contributors who made the new version of Flower possible (in git shortlog
order):
Aasheesh Singh
, Adam Narozniak
, Aml Hassan Esmil
, Charles Beauville
, Daniel J. Beutel
, Daniel Nata Nugraha
, Edoardo Gabrielli
, Gustavo Bertoli
, HelinLin
, Heng Pan
, Javier
, M S Chaitanya Kumar
, Mohammad Naseri
, Nikos Vlachakis
, Pritam Neog
, Robert Kuska
, Robert Steiner
, Taner Topal
, Yahia Salaheldin Shaaban
, Yan Gao
, Yasar Abbas
What’s new?#
Introduce stateful clients (experimental) (#2770, #2686, #2696, #2643, #2769)
Subclasses of
Client
andNumPyClient
can now store local state that remains on the client. Let’s start with the highlight first: this new feature is compatible with both simulated clients (viastart_simulation
) and networked clients (viastart_client
). It’s also the first preview of new abstractions likeContext
andRecordSet
. Clients can access state of typeRecordSet
viastate: RecordSet = self.context.state
. Changes to thisRecordSet
are preserved across different rounds of execution to enable stateful computations in a unified way across simulation and deployment.Improve performance (#2293)
Flower is faster than ever. All
FedAvg
-derived strategies now use in-place aggregation to reduce memory consumption. The Flower client serialization/deserialization has been rewritten from the ground up, which results in significant speedups, especially when the client-side training time is short.Support Federated Learning with Apple MLX and Flower (#2693)
Flower has official support for federated learning using Apple MLX via the new
quickstart-mlx
code example.Introduce new XGBoost cyclic strategy (#2666, #2668)
A new strategy called
FedXgbCyclic
supports a client-by-client style of training (often called cyclic). Thexgboost-comprehensive
code example shows how to use it in a full project. In addition to that,xgboost-comprehensive
now also supports simulation mode. With this, Flower offers best-in-class XGBoost support.Support Python 3.11 (#2394)
Framework tests now run on Python 3.8, 3.9, 3.10, and 3.11. This will ensure better support for users using more recent Python versions.
Update gRPC and ProtoBuf dependencies (#2814)
The
grpcio
andprotobuf
dependencies were updated to their latest versions for improved security and performance.Introduce Docker image for Flower server (#2700, #2688, #2705, #2695, #2747, #2746, #2680, #2682, #2701)
The Flower server can now be run using an official Docker image. A new how-to guide explains how to run Flower using Docker. An official Flower client Docker image will follow.
Introduce
flower-via-docker-compose
example (#2626)Introduce
quickstart-sklearn-tabular
example (#2719)Introduce
custom-metrics
example (#1958)Update code examples to use Flower Datasets (#2450, #2456, #2318, #2712)
Several code examples were updated to use Flower Datasets.
General updates to Flower Examples (#2381, #2805, #2782, #2806, #2829, #2825, #2816, #2726, #2659, #2655)
Many Flower code examples received substantial updates.
Update Flower Baselines
Improve documentation (#2674, #2480, #2826, #2727, #2761, #2900)
Improved testing and development infrastructure (#2797, #2676, #2644, #2656, #2848, #2675, #2735, #2767, #2732, #2744, #2681, #2699, #2745, #2734, #2731, #2652, #2720, #2721, #2717, #2864, #2694, #2709, #2658, #2796, #2692, #2657, #2813, #2661, #2398)
The Flower testing and development infrastructure has received substantial updates. This makes Flower 1.7 the most tested release ever.
Update dependencies (#2753, #2651, #2739, #2837, #2788, #2811, #2774, #2790, #2751, #2850, #2812, #2872, #2736, #2756, #2857, #2757, #2810, #2740, #2789)
General improvements (#2803, #2847, #2877, #2690, #2889, #2874, #2819, #2689, #2457, #2870, #2669, #2876, #2885, #2858, #2867, #2351, #2886, #2860, #2828, #2869, #2875, #2733, #2488, #2646, #2879, #2821, #2855, #2800, #2807, #2801, #2804, #2851, #2787, #2852, #2672, #2759)
Incompatible changes#
Deprecate
start_numpy_client
(#2563, #2718)Until now, clients of type
NumPyClient
needed to be started viastart_numpy_client
. In our efforts to consolidate framework APIs, we have introduced changes, and now all client types should start viastart_client
. To continue usingNumPyClient
clients, you simply need to first call the.to_client()
method and then pass returnedClient
object tostart_client
. The examples and the documentation have been updated accordingly.Deprecate legacy DP wrappers (#2749)
Legacy DP wrapper classes are deprecated, but still functional. This is in preparation for an all-new pluggable version of differential privacy support in Flower.
Make optional arg
--callable
inflower-client
a required positional arg (#2673)Rename
certificates
toroot_certificates
inDriver
(#2890)Drop experimental
Task
fields (#2866, #2865)Experimental fields
sa
,legacy_server_message
andlegacy_client_message
were removed fromTask
message. The removed fields are superseded by the newRecordSet
abstraction.Retire MXNet examples (#2724)
The development of the MXNet fremework has ended and the project is now archived on GitHub. Existing MXNet examples won’t receive updates.
v1.6.0 (2023-11-28)#
Thanks to our contributors#
We would like to give our special thanks to all the contributors who made the new version of Flower possible (in git shortlog
order):
Aashish Kolluri
, Adam Narozniak
, Alessio Mora
, Barathwaja S
, Charles Beauville
, Daniel J. Beutel
, Daniel Nata Nugraha
, Gabriel Mota
, Heng Pan
, Ivan Agarský
, JS.KIM
, Javier
, Marius Schlegel
, Navin Chandra
, Nic Lane
, Peterpan828
, Qinbin Li
, Shaz-hash
, Steve Laskaridis
, Taner Topal
, William Lindskog
, Yan Gao
, cnxdeveloper
, k3nfalt
What’s new?#
Add experimental support for Python 3.12 (#2565)
Add new XGBoost examples (#2612, #2554, #2617, #2618, #2619, #2567)
We have added a new
xgboost-quickstart
example alongside a newxgboost-comprehensive
example that goes more in-depth.Add Vertical FL example (#2598)
We had many questions about Vertical Federated Learning using Flower, so we decided to add an simple example for it on the Titanic dataset alongside a tutorial (in the README).
Support custom
ClientManager
instart_driver()
(#2292)Update REST API to support create and delete nodes (#2283)
Update the Android SDK (#2187)
Add gRPC request-response capability to the Android SDK.
Update the C++ SDK (#2537, #2528, #2523, #2522)
Add gRPC request-response capability to the C++ SDK.
Make HTTPS the new default (#2591, #2636)
Flower is moving to HTTPS by default. The new
flower-server
requires passing--certificates
, but users can enable--insecure
to use HTTP for prototyping. The same applies toflower-client
, which can either use user-provided credentials or gRPC-bundled certificates to connect to an HTTPS-enabled server or requires opt-out via passing--insecure
to enable insecure HTTP connections.For backward compatibility,
start_client()
andstart_numpy_client()
will still start in insecure mode by default. In a future release, insecure connections will require user opt-in by passinginsecure=True
.Unify client API (#2303, #2390, #2493)
Using the
client_fn
, Flower clients can interchangeably run as standalone processes (i.e. viastart_client
) or in simulation (i.e. viastart_simulation
) without requiring changes to how the client class is defined and instantiated. Theto_client()
function is introduced to convert aNumPyClient
to aClient
.Add new
Bulyan
strategy (#1817, #1891)The new
Bulyan
strategy implements Bulyan by El Mhamdi et al., 2018Add new
XGB Bagging
strategy (#2611)Update Flower Baselines
General updates to Flower Examples (#2384, #2425, #2526, #2302, #2545)
General updates to Flower Baselines (#2301, #2305, #2307, #2327, #2435, #2462, #2463, #2461, #2469, #2466, #2471, #2472, #2470)
General updates to the simulation engine (#2331, #2447, #2448, #2294)
General updates to Flower SDKs (#2288, #2429, #2555, #2543, #2544, #2597, #2623)
General improvements (#2309, #2310, #2313, #2316, #2317, #2349, #2360, #2402, #2446, #2561, #2273, #2267, #2274, #2275, #2432, #2251, #2321, #1936, #2408, #2413, #2401, #2531, #2534, #2535, #2521, #2553, #2596)
Flower received many improvements under the hood, too many to list here.
Incompatible changes#
Remove support for Python 3.7 (#2280, #2299, #2304, #2306, #2355, #2356)
Python 3.7 support was deprecated in Flower 1.5, and this release removes support. Flower now requires Python 3.8.
Remove experimental argument
rest
fromstart_client
(#2324)The (still experimental) argument
rest
was removed fromstart_client
andstart_numpy_client
. Usetransport="rest"
to opt into the experimental REST API instead.
v1.5.0 (2023-08-31)#
Thanks to our contributors#
We would like to give our special thanks to all the contributors who made the new version of Flower possible (in git shortlog
order):
Adam Narozniak
, Anass Anhari
, Charles Beauville
, Dana-Farber
, Daniel J. Beutel
, Daniel Nata Nugraha
, Edoardo Gabrielli
, Gustavo Bertoli
, Heng Pan
, Javier
, Mahdi
, Steven Hé (Sīchàng)
, Taner Topal
, achiverram28
, danielnugraha
, eunchung
, ruthgal
What’s new?#
Introduce new simulation engine (#1969, #2221, #2248)
The new simulation engine has been rewritten from the ground up, yet it remains fully backwards compatible. It offers much improved stability and memory handling, especially when working with GPUs. Simulations transparently adapt to different settings to scale simulation in CPU-only, CPU+GPU, multi-GPU, or multi-node multi-GPU environments.
Comprehensive documentation includes a new how-to run simulations guide, new simulation-pytorch and simulation-tensorflow notebooks, and a new YouTube tutorial series.
Restructure Flower Docs (#1824, #1865, #1884, #1887, #1919, #1922, #1920, #1923, #1924, #1962, #2006, #2133, #2203, #2215, #2122, #2223, #2219, #2232, #2233, #2234, #2235, #2237, #2238, #2242, #2231, #2243, #2227)
Much effort went into a completely restructured Flower docs experience. The documentation on flower.ai/docs is now divided into Flower Framework, Flower Baselines, Flower Android SDK, Flower iOS SDK, and code example projects.
Introduce Flower Swift SDK (#1858, #1897)
This is the first preview release of the Flower Swift SDK. Flower support on iOS is improving, and alongside the Swift SDK and code example, there is now also an iOS quickstart tutorial.
Introduce Flower Android SDK (#2131)
This is the first preview release of the Flower Kotlin SDK. Flower support on Android is improving, and alongside the Kotlin SDK and code example, there is now also an Android quickstart tutorial.
Introduce new end-to-end testing infrastructure (#1842, #2071, #2072, #2068, #2067, #2069, #2073, #2070, #2074, #2082, #2084, #2093, #2109, #2095, #2140, #2137, #2165)
A new testing infrastructure ensures that new changes stay compatible with existing framework integrations or strategies.
Deprecate Python 3.7
Since Python 3.7 reached its end of life (EOL) on 2023-06-27, support for Python 3.7 is now deprecated and will be removed in an upcoming release.
Add new
FedTrimmedAvg
strategy (#1769, #1853)The new
FedTrimmedAvg
strategy implements Trimmed Mean by Dong Yin, 2018.Introduce start_driver (#1697)
In addition to
start_server
and using the raw Driver API, there is a newstart_driver
function that allows for runningstart_server
scripts as a Flower driver with only a single-line code change. Check out themt-pytorch
code example to see a working example usingstart_driver
.Add parameter aggregation to
mt-pytorch
code example (#1785)The
mt-pytorch
example shows how to aggregate parameters when writing a driver script. The includeddriver.py
andserver.py
have been aligned to demonstrate both the low-level way and the high-level way of building server-side logic.Migrate experimental REST API to Starlette (2171)
The (experimental) REST API used to be implemented in FastAPI, but it has now been migrated to use Starlette directly.
Please note: The REST request-response API is still experimental and will likely change significantly over time.
Introduce experimental gRPC request-response API (#1867, #1901)
In addition to the existing gRPC API (based on bidirectional streaming) and the experimental REST API, there is now a new gRPC API that uses a request-response model to communicate with client nodes.
Please note: The gRPC request-response API is still experimental and will likely change significantly over time.
Replace the experimental
start_client(rest=True)
with the newstart_client(transport="rest")
(#1880)The (experimental)
start_client
argumentrest
was deprecated in favour of a new argumenttransport
.start_client(transport="rest")
will yield the same behaviour asstart_client(rest=True)
did before. All code should migrate to the new argumenttransport
. The deprecated argumentrest
will be removed in a future release.Add a new gRPC option (#2197)
We now start a gRPC server with the
grpc.keepalive_permit_without_calls
option set to 0 by default. This prevents the clients from sending keepalive pings when there is no outstanding stream.Improve example notebooks (#2005)
There’s a new 30min Federated Learning PyTorch tutorial!
Example updates (#1772, #1873, #1981, #1988, #1984, #1982, #2112, #2144, #2174, #2225, #2183)
Many examples have received significant updates, including simplified advanced-tensorflow and advanced-pytorch examples, improved macOS compatibility of TensorFlow examples, and code examples for simulation. A major upgrade is that all code examples now have a
requirements.txt
(in addition topyproject.toml
).General improvements (#1872, #1866, #1884, #1837, #1477, #2171)
Flower received many improvements under the hood, too many to list here.
Incompatible changes#
None
v1.4.0 (2023-04-21)#
Thanks to our contributors#
We would like to give our special thanks to all the contributors who made the new version of Flower possible (in git shortlog
order):
Adam Narozniak
, Alexander Viala Bellander
, Charles Beauville
, Chenyang Ma (Danny)
, Daniel J. Beutel
, Edoardo
, Gautam Jajoo
, Iacob-Alexandru-Andrei
, JDRanpariya
, Jean Charle Yaacoub
, Kunal Sarkhel
, L. Jiang
, Lennart Behme
, Max Kapsecker
, Michał
, Nic Lane
, Nikolaos Episkopos
, Ragy
, Saurav Maheshkar
, Semo Yang
, Steve Laskaridis
, Steven Hé (Sīchàng)
, Taner Topal
What’s new?#
Introduce support for XGBoost (
FedXgbNnAvg
strategy and example) (#1694, #1709, #1715, #1717, #1763, #1795)XGBoost is a tree-based ensemble machine learning algorithm that uses gradient boosting to improve model accuracy. We added a new
FedXgbNnAvg
strategy, and a code example that demonstrates the usage of this new strategy in an XGBoost project.Introduce iOS SDK (preview) (#1621, #1764)
This is a major update for anyone wanting to implement Federated Learning on iOS mobile devices. We now have a swift iOS SDK present under src/swift/flwr that will facilitate greatly the app creating process. To showcase its use, the iOS example has also been updated!
Introduce new “What is Federated Learning?” tutorial (#1657, #1721)
A new entry-level tutorial in our documentation explains the basics of Fedetated Learning. It enables anyone who’s unfamiliar with Federated Learning to start their journey with Flower. Forward it to anyone who’s interested in Federated Learning!
Introduce new Flower Baseline: FedProx MNIST (#1513, #1680, #1681, #1679)
This new baseline replicates the MNIST+CNN task from the paper Federated Optimization in Heterogeneous Networks (Li et al., 2018). It uses the
FedProx
strategy, which aims at making convergence more robust in heterogeneous settings.Introduce new Flower Baseline: FedAvg FEMNIST (#1655)
This new baseline replicates an experiment evaluating the performance of the FedAvg algorithm on the FEMNIST dataset from the paper LEAF: A Benchmark for Federated Settings (Caldas et al., 2018).
Introduce (experimental) REST API (#1594, #1690, #1695, #1712, #1802, #1770, #1733)
A new REST API has been introduced as an alternative to the gRPC-based communication stack. In this initial version, the REST API only supports anonymous clients.
Please note: The REST API is still experimental and will likely change significantly over time.
Improve the (experimental) Driver API (#1663, #1666, #1667, #1664, #1675, #1676, #1693, #1662, #1794)
The Driver API is still an experimental feature, but this release introduces some major upgrades. One of the main improvements is the introduction of an SQLite database to store server state on disk (instead of in-memory). Another improvement is that tasks (instructions or results) that have been delivered will now be deleted. This greatly improves the memory efficiency of a long-running Flower server.
Fix spilling issues related to Ray during simulations (#1698)
While running long simulations,
ray
was sometimes spilling huge amounts of data that would make the training unable to continue. This is now fixed! 🎉Add new example using
TabNet
and Flower (#1725)TabNet is a powerful and flexible framework for training machine learning models on tabular data. We now have a federated example using Flower: quickstart-tabnet.
Add new how-to guide for monitoring simulations (#1649)
We now have a documentation guide to help users monitor their performance during simulations.
Add training metrics to
History
object during simulations (#1696)The
fit_metrics_aggregation_fn
can be used to aggregate training metrics, but previous releases did not save the results in theHistory
object. This is now the case!General improvements (#1659, #1646, #1647, #1471, #1648, #1651, #1652, #1653, #1659, #1665, #1670, #1672, #1677, #1684, #1683, #1686, #1682, #1685, #1692, #1705, #1708, #1711, #1713, #1714, #1718, #1716, #1723, #1735, #1678, #1750, #1753, #1736, #1766, #1760, #1775, #1776, #1777, #1779, #1784, #1773, #1755, #1789, #1788, #1798, #1799, #1739, #1800, #1804, #1805)
Flower received many improvements under the hood, too many to list here.
Incompatible changes#
None
v1.3.0 (2023-02-06)#
Thanks to our contributors#
We would like to give our special thanks to all the contributors who made the new version of Flower possible (in git shortlog
order):
Adam Narozniak
, Alexander Viala Bellander
, Charles Beauville
, Daniel J. Beutel
, JDRanpariya
, Lennart Behme
, Taner Topal
What’s new?#
Add support for
workload_id
andgroup_id
in Driver API (#1595)The (experimental) Driver API now supports a
workload_id
that can be used to identify which workload a task belongs to. It also supports a newgroup_id
that can be used, for example, to indicate the current training round. Both theworkload_id
andgroup_id
enable client nodes to decide whether they want to handle a task or not.Make Driver API and Fleet API address configurable (#1637)
The (experimental) long-running Flower server (Driver API and Fleet API) can now configure the server address of both Driver API (via
--driver-api-address
) and Fleet API (via--fleet-api-address
) when starting:flower-server --driver-api-address "0.0.0.0:8081" --fleet-api-address "0.0.0.0:8086"
Both IPv4 and IPv6 addresses are supported.
Add new example of Federated Learning using fastai and Flower (#1598)
A new code example (
quickstart-fastai
) demonstrates federated learning with fastai and Flower. You can find it here: quickstart-fastai.Make Android example compatible with
flwr >= 1.0.0
and the latest versions of Android (#1603)The Android code example has received a substantial update: the project is compatible with Flower 1.0 (and later), the UI received a full refresh, and the project is updated to be compatible with newer Android tooling.
Add new
FedProx
strategy (#1619)This strategy is almost identical to
FedAvg
, but helps users replicate what is described in this paper. It essentially adds a parameter calledproximal_mu
to regularize the local models with respect to the global models.Add new metrics to telemetry events (#1640)
An updated event structure allows, for example, the clustering of events within the same workload.
Add new custom strategy tutorial section #1623
The Flower tutorial now has a new section that covers implementing a custom strategy from scratch: Open in Colab
Add new custom serialization tutorial section (#1622)
The Flower tutorial now has a new section that covers custom serialization: Open in Colab
General improvements (#1638, #1634, #1636, #1635, #1633, #1632, #1631, #1630, #1627, #1593, #1616, #1615, #1607, #1609, #1608, #1603, #1590, #1580, #1599, #1600, #1601, #1597, #1595, #1591, #1588, #1589, #1587, #1573, #1581, #1578, #1574, #1572, #1586)
Flower received many improvements under the hood, too many to list here.
Updated documentation (#1629, #1628, #1620, #1618, #1617, #1613, #1614)
As usual, the documentation has improved quite a bit. It is another step in our effort to make the Flower documentation the best documentation of any project. Stay tuned and as always, feel free to provide feedback!
Incompatible changes#
None
v1.2.0 (2023-01-13)#
Thanks to our contributors#
We would like to give our special thanks to all the contributors who made the new version of Flower possible (in git shortlog
order):
Adam Narozniak
, Charles Beauville
, Daniel J. Beutel
, Edoardo
, L. Jiang
, Ragy
, Taner Topal
, dannymcy
What’s new?#
Introduce new Flower Baseline: FedAvg MNIST (#1497, #1552)
Over the coming weeks, we will be releasing a number of new reference implementations useful especially to FL newcomers. They will typically revisit well known papers from the literature, and be suitable for integration in your own application or for experimentation, in order to deepen your knowledge of FL in general. Today’s release is the first in this series. Read more.
Improve GPU support in simulations (#1555)
The Ray-based Virtual Client Engine (
start_simulation
) has been updated to improve GPU support. The update includes some of the hard-earned lessons from scaling simulations in GPU cluster environments. New defaults make running GPU-based simulations substantially more robust.Improve GPU support in Jupyter Notebook tutorials (#1527, #1558)
Some users reported that Jupyter Notebooks have not always been easy to use on GPU instances. We listened and made improvements to all of our Jupyter notebooks! Check out the updated notebooks here:
Introduce optional telemetry (#1533, #1544, #1584)
After a request for feedback from the community, the Flower open-source project introduces optional collection of anonymous usage metrics to make well-informed decisions to improve Flower. Doing this enables the Flower team to understand how Flower is used and what challenges users might face.
Flower is a friendly framework for collaborative AI and data science. Staying true to this statement, Flower makes it easy to disable telemetry for users who do not want to share anonymous usage metrics. Read more..
Introduce (experimental) Driver API (#1520, #1525, #1545, #1546, #1550, #1551, #1567)
Flower now has a new (experimental) Driver API which will enable fully programmable, async, and multi-tenant Federated Learning and Federated Analytics applications. Phew, that’s a lot! Going forward, the Driver API will be the abstraction that many upcoming features will be built on - and you can start building those things now, too.
The Driver API also enables a new execution mode in which the server runs indefinitely. Multiple individual workloads can run concurrently and start and stop their execution independent of the server. This is especially useful for users who want to deploy Flower in production.
To learn more, check out the
mt-pytorch
code example. We look forward to you feedback!Please note: The Driver API is still experimental and will likely change significantly over time.
Add new Federated Analytics with Pandas example (#1469, #1535)
A new code example (
quickstart-pandas
) demonstrates federated analytics with Pandas and Flower. You can find it here: quickstart-pandas.Add new strategies: Krum and MultiKrum (#1481)
Edoardo, a computer science student at the Sapienza University of Rome, contributed a new
Krum
strategy that enables users to easily use Krum and MultiKrum in their workloads.Update C++ example to be compatible with Flower v1.2.0 (#1495)
The C++ code example has received a substantial update to make it compatible with the latest version of Flower.
General improvements (#1491, #1504, #1506, #1514, #1522, #1523, #1526, #1528, #1547, #1549, #1560, #1564, #1566)
Flower received many improvements under the hood, too many to list here.
Updated documentation (#1494, #1496, #1500, #1503, #1505, #1524, #1518, #1519, #1515)
As usual, the documentation has improved quite a bit. It is another step in our effort to make the Flower documentation the best documentation of any project. Stay tuned and as always, feel free to provide feedback!
One highlight is the new first time contributor guide: if you’ve never contributed on GitHub before, this is the perfect place to start!
Incompatible changes#
None
v1.1.0 (2022-10-31)#
Thanks to our contributors#
We would like to give our special thanks to all the contributors who made the new version of Flower possible (in git shortlog
order):
Akis Linardos
, Christopher S
, Daniel J. Beutel
, George
, Jan Schlicht
, Mohammad Fares
, Pedro Porto Buarque de Gusmão
, Philipp Wiesner
, Rob Luke
, Taner Topal
, VasundharaAgarwal
, danielnugraha
, edogab33
What’s new?#
Introduce Differential Privacy wrappers (preview) (#1357, #1460)
The first (experimental) preview of pluggable Differential Privacy wrappers enables easy configuration and usage of differential privacy (DP). The pluggable DP wrappers enable framework-agnostic and strategy-agnostic usage of both client-side DP and server-side DP. Head over to the Flower docs, a new explainer goes into more detail.
New iOS CoreML code example (#1289)
Flower goes iOS! A massive new code example shows how Flower clients can be built for iOS. The code example contains both Flower iOS SDK components that can be used for many tasks, and one task example running on CoreML.
New FedMedian strategy (#1461)
The new
FedMedian
strategy implements Federated Median (FedMedian) by Yin et al., 2018.Log
Client
exceptions in Virtual Client Engine (#1493)All
Client
exceptions happening in the VCE are now logged by default and not just exposed to the configuredStrategy
(via thefailures
argument).Improve Virtual Client Engine internals (#1401, #1453)
Some internals of the Virtual Client Engine have been revamped. The VCE now uses Ray 2.0 under the hood, the value type of the
client_resources
dictionary changed tofloat
to allow fractions of resources to be allocated.Support optional
Client
/NumPyClient
methods in Virtual Client EngineThe Virtual Client Engine now has full support for optional
Client
(andNumPyClient
) methods.Provide type information to packages using
flwr
(#1377)The package
flwr
is now bundled with apy.typed
file indicating that the package is typed. This enables typing support for projects or packages that useflwr
by enabling them to improve their code using static type checkers likemypy
.Updated code example (#1344, #1347)
The code examples covering scikit-learn and PyTorch Lightning have been updated to work with the latest version of Flower.
Updated documentation (#1355, #1558, #1379, #1380, #1381, #1332, #1391, #1403, #1364, #1409, #1419, #1444, #1448, #1417, #1449, #1465, #1467)
There have been so many documentation updates that it doesn’t even make sense to list them individually.
Restructured documentation (#1387)
The documentation has been restructured to make it easier to navigate. This is just the first step in a larger effort to make the Flower documentation the best documentation of any project ever. Stay tuned!
Open in Colab button (#1389)
The four parts of the Flower Federated Learning Tutorial now come with a new
Open in Colab
button. No need to install anything on your local machine, you can now use and learn about Flower in your browser, it’s only a single click away.Improved tutorial (#1468, #1470, #1472, #1473, #1474, #1475)
The Flower Federated Learning Tutorial has two brand-new parts covering custom strategies (still WIP) and the distinction between
Client
andNumPyClient
. The existing parts one and two have also been improved (many small changes and fixes).
Incompatible changes#
None
v1.0.0 (2022-07-28)#
Highlights#
Stable Virtual Client Engine (accessible via
start_simulation
)All
Client
/NumPyClient
methods are now optionalConfigurable
get_parameters
Tons of small API cleanups resulting in a more coherent developer experience
Thanks to our contributors#
We would like to give our special thanks to all the contributors who made Flower 1.0 possible (in reverse GitHub Contributors order):
@rtaiello, @g-pichler, @rob-luke, @andreea-zaharia, @kinshukdua, @nfnt, @tatiana-s, @TParcollet, @vballoli, @negedng, @RISHIKESHAVAN, @hei411, @SebastianSpeitel, @AmitChaulwar, @Rubiel1, @FANTOME-PAN, @Rono-BC, @lbhm, @sishtiaq, @remde, @Jueun-Park, @architjen, @PratikGarai, @mrinaald, @zliel, @MeiruiJiang, @sancarlim, @gubertoli, @Vingt100, @MakGulati, @cozek, @jafermarq, @sisco0, @akhilmathurs, @CanTuerk, @mariaboerner1987, @pedropgusmao, @tanertopal, @danieljanes.
Incompatible changes#
All arguments must be passed as keyword arguments (#1338)
Pass all arguments as keyword arguments, positional arguments are not longer supported. Code that uses positional arguments (e.g.,
start_client("127.0.0.1:8080", FlowerClient())
) must add the keyword for each positional argument (e.g.,start_client(server_address="127.0.0.1:8080", client=FlowerClient())
).Introduce configuration object
ServerConfig
instart_server
andstart_simulation
(#1317)Instead of a config dictionary
{"num_rounds": 3, "round_timeout": 600.0}
,start_server
andstart_simulation
now expect a configuration object of typeflwr.server.ServerConfig
.ServerConfig
takes the same arguments that as the previous config dict, but it makes writing type-safe code easier and the default parameters values more transparent.Rename built-in strategy parameters for clarity (#1334)
The following built-in strategy parameters were renamed to improve readability and consistency with other API’s:
fraction_eval
–>fraction_evaluate
min_eval_clients
–>min_evaluate_clients
eval_fn
–>evaluate_fn
Update default arguments of built-in strategies (#1278)
All built-in strategies now use
fraction_fit=1.0
andfraction_evaluate=1.0
, which means they select all currently available clients for training and evaluation. Projects that relied on the previous default values can get the previous behaviour by initializing the strategy in the following way:strategy = FedAvg(fraction_fit=0.1, fraction_evaluate=0.1)
Add
server_round
toStrategy.evaluate
(#1334)The
Strategy
methodevaluate
now receives the current round of federated learning/evaluation as the first parameter.Add
server_round
andconfig
parameters toevaluate_fn
(#1334)The
evaluate_fn
passed to built-in strategies likeFedAvg
now takes three parameters: (1) The current round of federated learning/evaluation (server_round
), (2) the model parameters to evaluate (parameters
), and (3) a config dictionary (config
).Rename
rnd
toserver_round
(#1321)Several Flower methods and functions (
evaluate_fn
,configure_fit
,aggregate_fit
,configure_evaluate
,aggregate_evaluate
) receive the current round of federated learning/evaluation as their first parameter. To improve reaability and avoid confusion with random, this parameter has been renamed fromrnd
toserver_round
.Move
flwr.dataset
toflwr_baselines
(#1273)The experimental package
flwr.dataset
was migrated to Flower Baselines.Remove experimental strategies (#1280)
Remove unmaintained experimental strategies (
FastAndSlow
,FedFSv0
,FedFSv1
).Rename
Weights
toNDArrays
(#1258, #1259)flwr.common.Weights
was renamed toflwr.common.NDArrays
to better capture what this type is all about.Remove antiquated
force_final_distributed_eval
fromstart_server
(#1258, #1259)The
start_server
parameterforce_final_distributed_eval
has long been a historic artefact, in this release it is finally gone for good.Make
get_parameters
configurable (#1242)The
get_parameters
method now accepts a configuration dictionary, just likeget_properties
,fit
, andevaluate
.Replace
num_rounds
instart_simulation
with newconfig
parameter (#1281)The
start_simulation
function now accepts a configuration dictionaryconfig
instead of thenum_rounds
integer. This improves the consistency betweenstart_simulation
andstart_server
and makes transitioning between the two easier.
What’s new?#
Support Python 3.10 (#1320)
The previous Flower release introduced experimental support for Python 3.10, this release declares Python 3.10 support as stable.
Make all
Client
andNumPyClient
methods optional (#1260, #1277)The
Client
/NumPyClient
methodsget_properties
,get_parameters
,fit
, andevaluate
are all optional. This enables writing clients that implement, for example, onlyfit
, but no other method. No need to implementevaluate
when using centralized evaluation!Enable passing a
Server
instance tostart_simulation
(#1281)Similar to
start_server
,start_simulation
now accepts a fullServer
instance. This enables users to heavily customize the execution of eperiments and opens the door to running, for example, async FL using the Virtual Client Engine.Update code examples (#1291, #1286, #1282)
Many code examples received small or even large maintenance updates, among them are
scikit-learn
simulation_pytorch
quickstart_pytorch
quickstart_simulation
quickstart_tensorflow
advanced_tensorflow
Remove the obsolete simulation example (#1328)
Removes the obsolete
simulation
example and renamesquickstart_simulation
tosimulation_tensorflow
so it fits withs the naming ofsimulation_pytorch
Update documentation (#1223, #1209, #1251, #1257, #1267, #1268, #1300, #1304, #1305, #1307)
One substantial documentation update fixes multiple smaller rendering issues, makes titles more succinct to improve navigation, removes a deprecated library, updates documentation dependencies, includes the
flwr.common
module in the API reference, includes support for markdown-based documentation, migrates the changelog from.rst
to.md
, and fixes a number of smaller details!Minor updates
v0.19.0 (2022-05-18)#
What’s new?#
Flower Baselines (preview): FedOpt, FedBN, FedAvgM (#919, #1127, #914)
The first preview release of Flower Baselines has arrived! We’re kickstarting Flower Baselines with implementations of FedOpt (FedYogi, FedAdam, FedAdagrad), FedBN, and FedAvgM. Check the documentation on how to use Flower Baselines. With this first preview release we’re also inviting the community to contribute their own baselines.
C++ client SDK (preview) and code example (#1111)
Preview support for Flower clients written in C++. The C++ preview includes a Flower client SDK and a quickstart code example that demonstrates a simple C++ client using the SDK.
Add experimental support for Python 3.10 and Python 3.11 (#1135)
Python 3.10 is the latest stable release of Python and Python 3.11 is due to be released in October. This Flower release adds experimental support for both Python versions.
Aggregate custom metrics through user-provided functions (#1144)
Custom metrics (e.g.,
accuracy
) can now be aggregated without having to customize the strategy. Built-in strategies support two new arguments,fit_metrics_aggregation_fn
andevaluate_metrics_aggregation_fn
, that allow passing custom metric aggregation functions.User-configurable round timeout (#1162)
A new configuration value allows the round timeout to be set for
start_server
andstart_simulation
. If theconfig
dictionary contains around_timeout
key (with afloat
value in seconds), the server will wait at leastround_timeout
seconds before it closes the connection.Enable both federated evaluation and centralized evaluation to be used at the same time in all built-in strategies (#1091)
Built-in strategies can now perform both federated evaluation (i.e., client-side) and centralized evaluation (i.e., server-side) in the same round. Federated evaluation can be disabled by setting
fraction_eval
to0.0
.Two new Jupyter Notebook tutorials (#1141)
Two Jupyter Notebook tutorials (compatible with Google Colab) explain basic and intermediate Flower features:
An Introduction to Federated Learning: Open in Colab
Using Strategies in Federated Learning: Open in Colab
New FedAvgM strategy (Federated Averaging with Server Momentum) (#1076)
The new
FedAvgM
strategy implements Federated Averaging with Server Momentum [Hsu et al., 2019].New advanced PyTorch code example (#1007)
A new code example (
advanced_pytorch
) demonstrates advanced Flower concepts with PyTorch.New JAX code example (#906, #1143)
A new code example (
jax_from_centralized_to_federated
) shows federated learning with JAX and Flower.Minor updates
New option to keep Ray running if Ray was already initialized in
start_simulation
(#1177)Add support for custom
ClientManager
as astart_simulation
parameter (#1171)New documentation for implementing strategies (#1097, #1175)
New mobile-friendly documentation theme (#1174)
Limit version range for (optional)
ray
dependency to include only compatible releases (>=1.9.2,<1.12.0
) (#1205)
Incompatible changes#
Remove deprecated support for Python 3.6 (#871)
Remove deprecated KerasClient (#857)
Remove deprecated no-op extra installs (#973)
Remove deprecated proto fields from
FitRes
andEvaluateRes
(#869)Remove deprecated QffedAvg strategy (replaced by QFedAvg) (#1107)
Remove deprecated DefaultStrategy strategy (#1142)
Remove deprecated support for eval_fn accuracy return value (#1142)
Remove deprecated support for passing initial parameters as NumPy ndarrays (#1142)
v0.18.0 (2022-02-28)#
What’s new?#
Improved Virtual Client Engine compatibility with Jupyter Notebook / Google Colab (#866, #872, #833, #1036)
Simulations (using the Virtual Client Engine through
start_simulation
) now work more smoothly on Jupyter Notebooks (incl. Google Colab) after installing Flower with thesimulation
extra (pip install flwr[simulation]
).New Jupyter Notebook code example (#833)
A new code example (
quickstart_simulation
) demonstrates Flower simulations using the Virtual Client Engine through Jupyter Notebook (incl. Google Colab).Client properties (feature preview) (#795)
Clients can implement a new method
get_properties
to enable server-side strategies to query client properties.Experimental Android support with TFLite (#865)
Android support has finally arrived in
main
! Flower is both client-agnostic and framework-agnostic by design. One can integrate arbitrary client platforms and with this release, using Flower on Android has become a lot easier.The example uses TFLite on the client side, along with a new
FedAvgAndroid
strategy. The Android client andFedAvgAndroid
are still experimental, but they are a first step towards a fully-fledged Android SDK and a unifiedFedAvg
implementation that integrated the new functionality fromFedAvgAndroid
.Make gRPC keepalive time user-configurable and decrease default keepalive time (#1069)
The default gRPC keepalive time has been reduced to increase the compatibility of Flower with more cloud environments (for example, Microsoft Azure). Users can configure the keepalive time to customize the gRPC stack based on specific requirements.
New differential privacy example using Opacus and PyTorch (#805)
A new code example (
opacus
) demonstrates differentially-private federated learning with Opacus, PyTorch, and Flower.New Hugging Face Transformers code example (#863)
A new code example (
quickstart_huggingface
) demonstrates usage of Hugging Face Transformers with Flower.New MLCube code example (#779, #1034, #1065, #1090)
A new code example (
quickstart_mlcube
) demonstrates usage of MLCube with Flower.SSL-enabled server and client (#842, #844, #845, #847, #993, #994)
SSL enables secure encrypted connections between clients and servers. This release open-sources the Flower secure gRPC implementation to make encrypted communication channels accessible to all Flower users.
Updated
FedAdam
andFedYogi
strategies (#885, #895)FedAdam
andFedAdam
match the latest version of the Adaptive Federated Optimization paper.Initialize
start_simulation
with a list of client IDs (#860)start_simulation
can now be called with a list of client IDs (clients_ids
, type:List[str]
). Those IDs will be passed to theclient_fn
whenever a client needs to be initialized, which can make it easier to load data partitions that are not accessible throughint
identifiers.Minor updates
Update
num_examples
calculation in PyTorch code examples in (#909)Expose Flower version through
flwr.__version__
(#952)start_server
inapp.py
now returns aHistory
object containing metrics from training (#974)Make
max_workers
(used byThreadPoolExecutor
) configurable (#978)Increase sleep time after server start to three seconds in all code examples (#1086)
Added a new FAQ section to the documentation (#948)
And many more under-the-hood changes, library updates, documentation changes, and tooling improvements!
Incompatible changes#
Removed
flwr_example
andflwr_experimental
from release build (#869)The packages
flwr_example
andflwr_experimental
have been deprecated since Flower 0.12.0 and they are not longer included in Flower release builds. The associated extras (baseline
,examples-pytorch
,examples-tensorflow
,http-logger
,ops
) are now no-op and will be removed in an upcoming release.
v0.17.0 (2021-09-24)#
What’s new?#
Experimental virtual client engine (#781 #790 #791)
One of Flower’s goals is to enable research at scale. This release enables a first (experimental) peek at a major new feature, codenamed the virtual client engine. Virtual clients enable simulations that scale to a (very) large number of clients on a single machine or compute cluster. The easiest way to test the new functionality is to look at the two new code examples called
quickstart_simulation
andsimulation_pytorch
.The feature is still experimental, so there’s no stability guarantee for the API. It’s also not quite ready for prime time and comes with a few known caveats. However, those who are curious are encouraged to try it out and share their thoughts.
New built-in strategies (#828 #822)
FedYogi - Federated learning strategy using Yogi on server-side. Implementation based on https://arxiv.org/abs/2003.00295
FedAdam - Federated learning strategy using Adam on server-side. Implementation based on https://arxiv.org/abs/2003.00295
New PyTorch Lightning code example (#617)
New Variational Auto-Encoder code example (#752)
New scikit-learn code example (#748)
New experimental TensorBoard strategy (#789)
Minor updates
Incompatible changes#
Disabled final distributed evaluation (#800)
Prior behaviour was to perform a final round of distributed evaluation on all connected clients, which is often not required (e.g., when using server-side evaluation). The prior behaviour can be enabled by passing
force_final_distributed_eval=True
tostart_server
.Renamed q-FedAvg strategy (#802)
The strategy named
QffedAvg
was renamed toQFedAvg
to better reflect the notation given in the original paper (q-FFL is the optimization objective, q-FedAvg is the proposed solver). Note the original (now deprecated)QffedAvg
class is still available for compatibility reasons (it will be removed in a future release).Deprecated and renamed code example
simulation_pytorch
tosimulation_pytorch_legacy
(#791)This example has been replaced by a new example. The new example is based on the experimental virtual client engine, which will become the new default way of doing most types of large-scale simulations in Flower. The existing example was kept for reference purposes, but it might be removed in the future.
v0.16.0 (2021-05-11)#
What’s new?#
New built-in strategies (#549)
(abstract) FedOpt
FedAdagrad
Custom metrics for server and strategies (#717)
The Flower server is now fully task-agnostic, all remaining instances of task-specific metrics (such as
accuracy
) have been replaced by custom metrics dictionaries. Flower 0.15 introduced the capability to pass a dictionary containing custom metrics from client to server. As of this release, custom metrics replace task-specific metrics on the server.Custom metric dictionaries are now used in two user-facing APIs: they are returned from Strategy methods
aggregate_fit
/aggregate_evaluate
and they enable evaluation functions passed to built-in strategies (viaeval_fn
) to return more than two evaluation metrics. Strategies can even return aggregated metrics dictionaries for the server to keep track of.Strategy implementations should migrate their
aggregate_fit
andaggregate_evaluate
methods to the new return type (e.g., by simply returning an empty{}
), server-side evaluation functions should migrate fromreturn loss, accuracy
toreturn loss, {"accuracy": accuracy}
.Flower 0.15-style return types are deprecated (but still supported), compatibility will be removed in a future release.
Migration warnings for deprecated functionality (#690)
Earlier versions of Flower were often migrated to new APIs, while maintaining compatibility with legacy APIs. This release introduces detailed warning messages if usage of deprecated APIs is detected. The new warning messages often provide details on how to migrate to more recent APIs, thus easing the transition from one release to another.
MXNet example and documentation
FedBN implementation in example PyTorch: From Centralized To Federated (#696 #702 #705)
Incompatible changes#
Serialization-agnostic server (#721)
The Flower server is now fully serialization-agnostic. Prior usage of class
Weights
(which represents parameters as deserialized NumPy ndarrays) was replaced by classParameters
(e.g., inStrategy
).Parameters
objects are fully serialization-agnostic and represents parameters as byte arrays, thetensor_type
attributes indicates how these byte arrays should be interpreted (e.g., for serialization/deserialization).Built-in strategies implement this approach by handling serialization and deserialization to/from
Weights
internally. Custom/3rd-party Strategy implementations should update to the slightly changed Strategy method definitions. Strategy authors can consult PR #721 to see how strategies can easily migrate to the new format.Deprecated
flwr.server.Server.evaluate
, useflwr.server.Server.evaluate_round
instead (#717)
v0.15.0 (2021-03-12)#
What’s new?
Server-side parameter initialization (#658)
Model parameters can now be initialized on the server-side. Server-side parameter initialization works via a new
Strategy
method calledinitialize_parameters
.Built-in strategies support a new constructor argument called
initial_parameters
to set the initial parameters. Built-in strategies will provide these initial parameters to the server on startup and then delete them to free the memory afterwards.# Create model model = tf.keras.applications.EfficientNetB0( input_shape=(32, 32, 3), weights=None, classes=10 ) model.compile("adam", "sparse_categorical_crossentropy", metrics=["accuracy"]) # Create strategy and initialize parameters on the server-side strategy = fl.server.strategy.FedAvg( # ... (other constructor arguments) initial_parameters=model.get_weights(), ) # Start Flower server with the strategy fl.server.start_server("[::]:8080", config={"num_rounds": 3}, strategy=strategy)
If no initial parameters are provided to the strategy, the server will continue to use the current behaviour (namely, it will ask one of the connected clients for its parameters and use these as the initial global parameters).
Deprecations
Deprecate
flwr.server.strategy.DefaultStrategy
(migrate toflwr.server.strategy.FedAvg
, which is equivalent)
v0.14.0 (2021-02-18)#
What’s new?
Generalized
Client.fit
andClient.evaluate
return values (#610 #572 #633)Clients can now return an additional dictionary mapping
str
keys to values of the following types:bool
,bytes
,float
,int
,str
. This means one can return almost arbitrary values fromfit
/evaluate
and make use of them on the server side!This improvement also allowed for more consistent return types between
fit
andevaluate
:evaluate
should now return a tuple(float, int, dict)
representing the loss, number of examples, and a dictionary holding arbitrary problem-specific values like accuracy.In case you wondered: this feature is compatible with existing projects, the additional dictionary return value is optional. New code should however migrate to the new return types to be compatible with upcoming Flower releases (
fit
:List[np.ndarray], int, Dict[str, Scalar]
,evaluate
:float, int, Dict[str, Scalar]
). See the example below for details.Code example: note the additional dictionary return values in both
FlwrClient.fit
andFlwrClient.evaluate
:class FlwrClient(fl.client.NumPyClient): def fit(self, parameters, config): net.set_parameters(parameters) train_loss = train(net, trainloader) return net.get_weights(), len(trainloader), {"train_loss": train_loss} def evaluate(self, parameters, config): net.set_parameters(parameters) loss, accuracy, custom_metric = test(net, testloader) return loss, len(testloader), {"accuracy": accuracy, "custom_metric": custom_metric}
Generalized
config
argument inClient.fit
andClient.evaluate
(#595)The
config
argument used to be of typeDict[str, str]
, which means that dictionary values were expected to be strings. The new release generalizes this to enable values of the following types:bool
,bytes
,float
,int
,str
.This means one can now pass almost arbitrary values to
fit
/evaluate
using theconfig
dictionary. Yay, no morestr(epochs)
on the server-side andint(config["epochs"])
on the client side!Code example: note that the
config
dictionary now contains non-str
values in bothClient.fit
andClient.evaluate
:class FlwrClient(fl.client.NumPyClient): def fit(self, parameters, config): net.set_parameters(parameters) epochs: int = config["epochs"] train_loss = train(net, trainloader, epochs) return net.get_weights(), len(trainloader), {"train_loss": train_loss} def evaluate(self, parameters, config): net.set_parameters(parameters) batch_size: int = config["batch_size"] loss, accuracy = test(net, testloader, batch_size) return loss, len(testloader), {"accuracy": accuracy}
v0.13.0 (2021-01-08)#
What’s new?
New example: PyTorch From Centralized To Federated (#549)
Improved documentation
Bugfix:
v0.12.0 (2020-12-07)#
Important changes:
v0.11.0 (2020-11-30)#
Incompatible changes:
Renamed strategy methods (#486) to unify the naming of Flower’s public APIs. Other public methods/functions (e.g., every method in
Client
, but alsoStrategy.evaluate
) do not use theon_
prefix, which is why we’re removing it from the four methods in Strategy. To migrate rename the followingStrategy
methods accordingly:on_configure_evaluate
=>configure_evaluate
on_aggregate_evaluate
=>aggregate_evaluate
on_configure_fit
=>configure_fit
on_aggregate_fit
=>aggregate_fit
Important changes: