DLwbm123/FL-bench

Benchmark of federated learning. Dedicated to the community. 🤗

Federated Learning Benchmark

This is a benchmark for evaluating well-known federated learning (FL) and personalized federated learning (pFL) methods. This benchmark is not complicated and easy to extend.

Environment Preparation 🚀

I offer three approaches to install all required packages.

1. pip install -r requirements.txt

2. conda env create -f environment.yml

3. poetry (Recommended 👍. To keep all dependencies you install are identical to mine.)

   • If you are new to poetry 👀:
     1. pip install poetry
     2. cd to the root directory of FL-bench and run poetry install
     3. Run poetry shell to enter the virtual environement or poetry run ${your_command}.
     4. Have fun. 🤗
   • If you are not at China mainland:
     1. Comment mirror related configs in pyproject.toml.
     2. Run poetry lock --no-update to update the poetry.lock.

Method 🧬

Traditional FL Methods

• FedAvg -- Communication-Efficient Learning of Deep Networks from Decentralized Data (AISTATS'17)

• FedAvgM -- Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification (ArXiv'19)

• FedProx -- Federated Optimization in Heterogeneous Networks (MLSys'20)

• SCAFFOLD -- SCAFFOLD: Stochastic Controlled Averaging for Federated Learning (ICML'20)

• MOON -- Model-Contrastive Federated Learning (CVPR'21)

• FedDyn -- Federated Learning Based on Dynamic Regularization (ICLR'21)

• FedLC -- Federated Learning with Label Distribution Skew via Logits Calibration (ICML'22)

• FedGen -- Data-Free Knowledge Distillation for Heterogeneous Federated Learning (ICML'21)

Personalized FL Methods

• pFedSim (My Work⭐) -- pFedSim: Similarity-Aware Model Aggregation Towards Personalized Federated Learning (ArXiv'23)

• Local-Only -- Local training only (without communication).

• FedMD -- FedMD: Heterogenous Federated Learning via Model Distillation (NIPS'19)

• APFL -- Adaptive Personalized Federated Learning (ArXiv'20)

• LG-FedAvg -- Think Locally, Act Globally: Federated Learning with Local and Global Representations (ArXiv'20)

• FedBN -- FedBN: Federated Learning On Non-IID Features Via Local Batch Normalization (ICLR'21)

• FedPer -- Federated Learning with Personalization Layers (AISTATS'20)

• FedRep -- Exploiting Shared Representations for Personalized Federated Learning (ICML'21)

• Per-FedAvg -- Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach (NIPS'20)

• pFedMe -- Personalized Federated Learning with Moreau Envelopes (NIPS'20)

• Ditto -- Ditto: Fair and Robust Federated Learning Through Personalization (ICML'21)

• pFedHN -- Personalized Federated Learning using Hypernetworks (ICML'21)

• pFedLA -- Layer-Wised Model Aggregation for Personalized Federated Learning (CVPR'22)

• CFL -- Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints (ArXiv'19)

• FedFomo -- Personalized Federated Learning with First Order Model Optimization (ICLR'21)

• FedBabu -- FedBabu: Towards Enhanced Representation for Federated Image Classification (ICLR'22)

• FedAP -- Personalized Federated Learning with Adaptive Batchnorm for Healthcare (IEEE'22)

• kNN-Per -- Personalized Federated Learning through Local Memorization (ICML'22)

• MetaFed -- MetaFed: Federated Learning among Federations with Cyclic Knowledge Distillation for Personalized Healthcare (IJCAI'22)

More reproductions/features would come soon or later (depends on my mood 🤣).

Easy Run 🏃‍♂️

ALL classes of methods are inherited from FedAvgServer and FedAvgClient. If you wanna figure out the entire workflow and detail of variable settings, go check ./src/server/fedavg.py and ./src/client/fedavg.py.

# partition the CIFAR-10 according to Dir(0.1) for 100 clients
cd data
python generate_data.py -d cifar10 -a 0.1 -cn 100
cd ../

# run FedAvg under default setting.
cd src/server
python fedavg.py

About methods of generating federated dastaset, go check data/README.md for full details.

Monitor 📈 (optional and recommended 👍)

1. Run python -m visdom.server on terminal.
2. Run src/server/${algo}.py --visible 1
3. Go check localhost:8097 on your browser.

Generic Arguments 🔧

📢 All generic arguments have their default value. Go check get_fedavg_argparser() in FL-bench/src/server/fedavg.py for full details about generic arguments.

About the default values and hyperparameters of advanced FL methods, go check corresponding FL-bench/src/server/${algo}.py for full details.

Argument	Description
--dataset	The name of dataset that experiment run on.
--model	The model backbone experiment used.
--seed	Random seed for running experiment.
--join_ratio	Ratio for (client each round) / (client num in total).
--global_epoch	Global epoch, also called communication round.
--local_epoch	Local epoch for client local training.
--finetune_epoch	Epoch for clients fine-tunning their models before test.
--test_gap	Interval round of performing test on clients.
--eval_test	Non-zero value for performing evaluation on joined clients' testset before and after local training.
--eval_train	Non-zero value for performing evaluation on joined clients' trainset before and after local training.
--local_lr	Learning rate for client local training.
--momentum	Momentum for client local opitimizer.
--weight_decay	Weight decay for client local optimizer.
--verbose_gap	Interval round of displaying clients training performance on terminal.
--batch_size	Data batch size for client local training.
--use_cuda	Non-zero value indicates that tensors are in gpu.
--visible	Non-zero value for using Visdom to monitor algorithm performance on localhost:8097.
--global_testset	Non-zero value for evaluating client models over the global testset before and after local training, instead of evaluating over clients own testset. The global testset is the union set of all client's testset. NOTE: Activating this setting will considerably slow down the entire training process, especially the dataset is big.
--save_log	Non-zero value for saving algorithm running log in FL-bench/out/${algo}.
--straggler_ratio	The ratio of stragglers (set in [0, 1]). Stragglers would not perform full-epoch local training as normal clients. Their local epoch would be randomly selected from range [--straggler_min_local_epoch, --local_epoch).
--straggler_min_local_epoch	The minimum value of local epoch for stragglers.
--save_model	Non-zero value for saving output model(s) parameters in FL-bench/out/${algo}.
--save_fig	Non-zero value for saving the accuracy curves showed on Visdom into a .jpeg file at FL-bench/out/${algo}.
--save_metrics	Non-zero value for saving metrics stats into a .csv file at FL-bench/out/${algo}.

Supported Datasets 🎨

This benchmark only support algorithms to solve image classification task for now.

Regular Image Datasets

• MNIST (1 x 28 x 28, 10 classes)

• CIFAR-10/100 (3 x 32 x 32, 10/100 classes)

• EMNIST (1 x 28 x 28, 62 classes)

• FashionMNIST (1 x 28 x 28, 10 classes)

• Syhthetic Dataset

• FEMNIST (1 x 28 x 28, 62 classes)

• CelebA (3 x 218 x 178, 2 classes)

• SVHN (3 x 32 x 32, 10 classes)

• USPS (1 x 16 x 16, 10 classes)

• Tiny-ImageNet-200 (3 x 64 x 64, 200 classes)

• CINIC-10 (3 x 32 x 32, 10 classes)

• DomainNet (3 x ? x ?, 345 classes)

  • Go check data/README.md for the full process guideline 🧾.

Medical Image Datasets

• COVID-19 (3 x 244 x 224, 4 classes)

• Organ-S/A/CMNIST (1 x 28 x 28, 11 classes)

Acknowledgement 🤗

This benchmark does not contain the feature/method you interested? Describe them in here. I can't guarantee your request will be accomplised in time or even considered. So feel free to do it! 💡

Some reproductions in this benchmark are referred to https://github.com/TsingZ0/PFL-Non-IID, which is a great FL benchmark. 👍

This benchmark is still young, which means I will update it frequently and unpredictably. Therefore, periodically fetching the latest code is recommended. 🤖

If this benchmark is helpful to your research, it's my pleasure. 😏