FLGo: A Lightning Framework for Federated Learning

Introduction

FLGo is a library to conduct experiments about Federated Learning (FL). It is strong and reusable for research on FL, providing comprehensive easy-to-use modules to hold out for those who want to do various federated learning experiments.

Installation

• Install FLGo through pip. It's recommended to install pytorch by yourself before installing this library.

pip install flgo --upgrade

• Install FLGo through git

git clone https://github.com/WwZzz/easyFL.git

Join Us 😃

Welcome to our FLGo's WeChat group/QQ Group for more technical discussion.

Tutorials in Chinese can be found here

News

[2024.9.20] We present a comprehensive benchmark gallery here

[2024.8.01] Improving efficiency by sharing datasets across multiple processes within each task in the shared memory

Quick Start with 3 lines ⚡

import flgo
import flgo.benchmark.mnist_classification as mnist
import flgo.benchmark.partition as fbp
import flgo.algorithm.fedavg as fedavg

# Line 1: Create a typical federated learning task
flgo.gen_task_by_(mnist, fbp.IIDPartitioner(num_clients=100), './my_task')

# Line 2: Running FedAvg on this task
fedavg_runner = flgo.init('./my_task', fedavg, {'gpu': [0,], 'num_rounds':20, 'num_epochs': 1})

# Line 3: Start Training
fedavg_runner.run()

We take a classical federated dataset, Federated MNIST, as the example. The MNIST dataset is splitted into 100 parts identically and independently.

Line 1 creates the federated dataset as ./my_task and visualizes it in ./my_task/res.png

Lines 2 and 3 start the training procedure and outputs information to the console

2024-04-15 02:30:43,763 fflow.py init [line:642] INFO PROCESS ID:	552206
2024-04-15 02:30:43,763 fflow.py init [line:643] INFO Initializing devices: cuda:0 will be used for this running.
2024-04-15 02:30:43,763 fflow.py init [line:646] INFO BENCHMARK:	flgo.benchmark.mnist_classification
2024-04-15 02:30:43,763 fflow.py init [line:647] INFO TASK:			./my_task
2024-04-15 02:30:43,763 fflow.py init [line:648] INFO MODEL:		flgo.benchmark.mnist_classification.model.cnn
2024-04-15 02:30:43,763 fflow.py init [line:649] INFO ALGORITHM:	fedavg
2024-04-15 02:30:43,774 fflow.py init [line:688] INFO SCENE:		horizontal FL with 1 <class 'flgo.algorithm.fedbase.BasicServer'>, 100 <class 'flgo.algorithm.fedbase.BasicClient'>
2024-04-15 02:30:47,851 fflow.py init [line:705] INFO SIMULATOR:	<class 'flgo.simulator.default_simulator.Simulator'>
2024-04-15 02:30:47,853 fflow.py init [line:718] INFO Ready to start.
...
2024-04-15 02:30:52,466 fedbase.py run [line:253] INFO --------------Round 1--------------
2024-04-15 02:30:52,466 simple_logger.py log_once [line:14] INFO Current_time:1
2024-04-15 02:30:54,402 simple_logger.py log_once [line:28] INFO test_accuracy                 0.6534
2024-04-15 02:30:54,402 simple_logger.py log_once [line:28] INFO test_loss                     1.5835
...

• Show Training Result (optional)

import flgo.experiment.analyzer as fea
# Create the analysis plan
analysis_plan = {
    'Selector':{'task': './my_task', 'header':['fedavg',], },
    'Painter':{'Curve':[{'args':{'x':'communication_round', 'y':'val_loss'}}]},
}

fea.show(analysis_plan)

Each training result will be saved as a record under ./my_task/record. We can use the built-in analyzer to read and show it.

Why Using FLGo? 🛠️

Simulate Real-World System Heterogeneity 📱

Our FLGo supports running different algorithms in virtual environments like real-world. For example, clients in practice may

• be sometime inavailable,
• response to the server very slow,
• accidiently lose connection,
• upload incomplete model updates,
• ...

All of these behavior can be easily realized by integrating a simple Simulator to the runner like

import flgo
from flgo.simulator import ExampleSimulator
import flgo.algorithm.fedavg as fedavg

fedavg_runner = flgo.init('./my_task', fedavg, {'gpu': [0,]}, simulator=ExampleSimulator)
fedavg_runner.run()

Simulator is fully customizable and can fairly reflect the impact of system heterogeneity on different algorithms. Please refer to Paper or Tutorial for more details.

Comprehensive Benchmarks 👩‍👩‍👦‍👦

FLGo provides more than 50 benchmarks across different data types, different communication topology,...

	Task	Scenario	Datasets
CV	Classification	Horizontal & Vertical	CIFAR10\100, MNIST, FashionMNIST,FEMNIST, EMNIST, SVHN
	Detection	Horizontal	Coco, VOC
	Segmentation	Horizontal	Coco, SBDataset
NLP	Classification	Horizontal	Sentiment140, AG_NEWS, sst2
	Text Prediction	Horizontal	Shakespeare, Reddit
	Translation	Horizontal	Multi30k
Graph	Node Classification	Horizontal	Cora, Citeseer, Pubmed
	Link Prediction	Horizontal	Cora, Citeseer, Pubmed
	Graph Classification	Horizontal	Enzymes, Mutag
Recommendation	Rating Prediction	Horizontal & Vertical	Ciao, Movielens, Epinions, Filmtrust, Douban
Series	Time series forecasting	Horizontal	Electricity, Exchange Rate
Tabular	Classification	Horizontal	Adult, Bank Marketing
Synthetic	Regression	Horizontal	Synthetic, DistributedQP, CUBE

Usage

Each benchmark can be used to generate federated tasks that denote distributed scenes with specific data distributions like

import flgo
import flgo.benchmark.cifar10_classification as cifar10
import flgo.benchmark.partition as fbp
import flgo.algorithm.fedavg as fedavg

task = './my_first_cifar' # task name
flgo.gen_task_by_(cifar10, fbp.IIDPartitioner(num_clients=10), task) # generate task from benchmark with partitioner
flgo.init(task, fedavg, {'gpu':0}).run()

Visualized Data Heterogeneity 👀

We realize data heterogeneity by flexible partitioners. These partitioners can be easily combined with benchmark to generate federated tasks with different data distributions.

import flgo.benchmark.cifar10_classification as cifar10
import flgo.benchmark.partition as fbp

Dirichlet(α) of labels

flgo.gen_task_by_(cifar10, fbp.DirichletPartitioner(num_clients=100, alpha=0.1), 'dir0.1_cifar')
flgo.gen_task_by_(cifar10, fbp.DirichletPartitioner(num_clients=100, alpha=1.0), 'dir1.0_cifar')
flgo.gen_task_by_(cifar10, fbp.DirichletPartitioner(num_clients=100, alpha=5.0), 'dir5.0_cifar')
flgo.gen_task_by_(cifar10, fbp.DirichletPartitioner(num_clients=100, alpha=10.0), 'dir10.0_cifar')

Controllable Data Imbalance

# set imbalance=0.1, 0.3, 0.6 or 1.0
flgo.gen_task_by_(cifar10, fbp.DirichletPartitioner(num_clients=100, alpha=1.0, imbalance=0.1), 'dir1.0_cifar_imb0.1')
flgo.gen_task_by_(cifar10, fbp.DirichletPartitioner(num_clients=100, alpha=1.0, imbalance=0.3), 'dir1.0_cifar_imb0.3')
flgo.gen_task_by_(cifar10, fbp.DirichletPartitioner(num_clients=100, alpha=1.0, imbalance=0.6), 'dir1.0_cifar_imb0.6')
flgo.gen_task_by_(cifar10, fbp.DirichletPartitioner(num_clients=100, alpha=1.0, imbalance=1.0), 'dir1.0_cifar_imb1.0')

Heterogeneous Label Diversity

flgo.gen_task_by_(cifar10, fbp.DiversityPartitioner(num_clients=100, diversity=0.1), 'div0.1_cifar')
flgo.gen_task_by_(cifar10, fbp.DiversityPartitioner(num_clients=100, diversity=0.3), 'div0.3_cifar')
flgo.gen_task_by_(cifar10, fbp.DiversityPartitioner(num_clients=100, diversity=0.6), 'div0.6_cifar')
flgo.gen_task_by_(cifar10, fbp.DiversityPartitioner(num_clients=100, diversity=1.0), 'div1.0_cifar')

Partitioner is also customizable in flgo. We have provided a detailed example in this Tutorial.

Reproduction of Algorithms from TOP-tiers and Journals 🥇

We have realized more than 50 algorithms from TOP-tiers and Journals. The algorithms are listed as below

Classical FL & Data Heterogeneity

Method	Reference	Publication
FedAvg	link	AISTAS2017
FedProx	link	MLSys 2020
Scaffold	link	ICML 2020
FedDyn	link	ICLR 2021
MOON	link	CVPR 2021
FedNova	link	NIPS 2021
FedAvgM	link	arxiv
GradMA	link	CVPR 2023

Personalized FL

Method	Reference	Publication
Standalone	link	-
FedAvg+FineTune	-	-
Ditto	link	ICML 2021
FedALA	link	AAAI 2023
FedRep	link	ICML 2021
pFedMe	link	NIPS 2020
Per-FedAvg	link	NIPS 2020
FedAMP	link	AAAI 2021
FedFomo	link	ICLR 2021
LG-FedAvg	link	NIPS 2019 workshop
pFedHN	link	ICML 2021
Fed-ROD	link	ICLR 2023
FedPAC	link	ICLR 2023
FedPer	link	AISTATS 2020
APPLE	link	IJCAI 2022
FedBABU	link	ICLR 2022
FedBN	link	ICLR 2021
FedPHP	link	ECML/PKDD 2021
APFL	link	arxiv
FedProto	link	AAAI 2022
FedCP	link	KDD 2023
GPFL	link	ICCV 2023
pFedPara	link	ICLR 2022
FedFA	link	ICLR 2023

Fairness-Aware FL

Method	Reference	Publication
AFL	link	ICML 2019
FedFv	link	IJCAI 2021
FedFa	link	Information Sciences 2022
FedMgda+	link	IEEE TNSE 2022
QFedAvg	link	ICLR 2020

Asynchronous FL

Method	Reference	Publication
FedAsync	link	arxiv
FedBuff	link	AISTATS 2022
CA2FL	link	ICLR2024

Client Sampling & Heterogeneous Availability

Method	Reference	Publication
MIFA	link	NeurIPS 2021
PowerofChoice	link	arxiv
FedGS	link	AAAI 2023
ClusteredSampling	link	ICML 2021

Capacity Heterogeneity

Method	Reference	Publication
FederatedDropout	link	arxiv
FedRolex	link	NIPS 2022
Fjord	link	NIPS 2021
FLANC	link	NIPS 2022
Hermes	link	MobiCom 2021
FedMask	link	SenSys 2021
LotteryFL	link	arxiv
HeteroFL	link	ICLR 2021
TailorFL	link	SenSys 2022
pFedGate	link	ICML 2023

Combine All The Things Together! 🧱

FLGo supports flexible combinations of benchmarks, partitioners, algorithms and simulators , which are independent to each other and thus can be used like plugins. We have provided these plugins here , where each can be immediately downloaded and used by API

import flgo
import flgo.benchmark.partition as fbp

fedavg = flgo.download_resource(root='.', name='fedavg', type='algorithm')
mnist = flgo.download_resource(root='.', name='mnist_classification', type='benchmark')
task = 'test_down_mnist'
flgo.gen_task_by_(mnist,fbp.IIDPartitioner(num_clients=10,), task_path=task)
flgo.init(task, fedavg, {'gpu':0}).run()

Easy-to-use Experimental Tools 🧰

Load Results

Each runned result will be automatically saved in task_path/record/. We provide an API to easily load and filter records.

import flgo
import flgo.experiment.analyzer as fea
import matplotlib.pyplot as plt
res = fea.Selector({'task': './my_task', 'header':['fedavg',], },)
log_data = res.records['./my_task'][0].data
val_loss = log_data['val_loss']
plt.plot(list(range(len(val_loss))), val_loss)
plt.show()

Use Checkpoint

import flgo.algorithm.fedavg as fedavg
import flgo.experiment.analyzer

task = './my_task'
ckpt = '1'
runner = flgo.init(task, fedavg, {'gpu':[0,],'log_file':True, 'num_epochs':1, 'save_checkpoint':ckpt, 'load_checkpoint':ckpt})
runner.run()

We save each checkpoint at task_path/checkpoint/checkpoint_name/. By specifying the name of checkpoints, the training can be automatically recovered from them.

import flgo.algorithm.fedavg as fedavg
# the two methods need to be extended when using other algorithms
class Server(fedavg.Server): 
    def save_checkpoint(self):
        cpt = {
            'round': self.current_round,                           # current communication round
            'learning_rate': self.learning_rate,                   # learning rate
            'model_state_dict': self.model.state_dict(),           # model
            'early_stop_option': {                                 # early stop option
                '_es_best_score': self.gv.logger._es_best_score,
                '_es_best_round': self.gv.logger._es_best_round,
                '_es_patience': self.gv.logger._es_patience,
            },
            'output': self.gv.logger.output,                       # recorded information by Logger
            'time': self.gv.clock.current_time,                    # virtual time
        }
        return cpt

    def load_checkpoint(self, cpt):
        md = cpt.get('model_state_dict', None)
        round = cpt.get('round', None)
        output = cpt.get('output', None)
        early_stop_option = cpt.get('early_stop_option', None)
        time = cpt.get('time', None)
        learning_rate = cpt.get('learning_rate', None)
        if md is not None: self.model.load_state_dict(md)
        if round is not None: self.current_round = round + 1
        if output is not None: self.gv.logger.output = output
        if time is not None: self.gv.clock.set_time(time)
        if learning_rate is not None: self.learning_rate = learning_rate
        if early_stop_option is not None:
            self.gv.logger._es_best_score = early_stop_option['_es_best_score']
            self.gv.logger._es_best_round = early_stop_option['_es_best_round']
            self.gv.logger._es_patience = early_stop_option['_es_patience']

Note: different FL algorithms need to save different types of checkpoints. Here we only provide checkpoint save&load mechanism of FedAvg. We remain two APIs for customization above:

Use Logger

We show how to use customized Logger Here

Tutorials and Documents 📃

We have provided comprehensive Tutorials and Document for FLGo.

Deployment To Real Machines 💻

Our FLGo is able to be extended to real-world application. We provide a simple Example to show how to run FLGo on multiple machines.

Overview 📓

Options

Basic options:

• task is to choose the task of splited dataset. Options: name of fedtask (e.g. mnist_classification_client100_dist0_beta0_noise0).

• algorithm is to choose the FL algorithm. Options: fedfv, fedavg, fedprox, …

• model should be the corresponding model of the dataset. Options: mlp, cnn, resnet18.

Server-side options:

• sample decides the way to sample clients in each round. Options: uniform means uniformly, md means choosing with probability.

• aggregate decides the way to aggregate clients' model. Options: uniform, weighted_scale, weighted_com

• num_rounds is the number of communication rounds.

• proportion is the proportion of clients to be selected in each round.

• lr_scheduler is the global learning rate scheduler.

• learning_rate_decay is the decay rate of the learning rate.

Client-side options:

• num_epochs is the number of local training epochs.

• num_steps is the number of local updating steps and the default value is -1. If this term is set larger than 0, num_epochs is not valid.

• learning_rate is the step size when locally training.

• batch_size is the size of one batch data during local training. batch_size = full_batch if batch_size==-1 and batch_size=|Di|*batch_size if 1>batch_size>0.

• optimizer is to choose the optimizer. Options: SGD, Adam.

• weight_decay is to set ratio for weight decay during the local training process.

• momentum is the ratio of the momentum item when the optimizer SGD taking each step.

Real Machine-Dependent options:

• seed is the initial random seed.

• gpu is the id of the GPU device. (e.g. CPU is used without specifying this term. --gpu 0 will use device GPU 0, and --gpu 0 1 2 3 will use the specified 4 GPUs when num_threads>0.

• server_with_cpu is set False as default value,..

• test_batch_size is the batch_size used when evaluating models on validation datasets, which is limited by the free space of the used device.

• eval_interval controls the interval between every two evaluations.

• num_threads is the number of threads in the clients computing session that aims to accelerate the training process.

• num_workers is the number of workers of the torch.utils.data.Dataloader

Additional hyper-parameters for particular federated algorithms:

• algo_para is used to receive the algorithm-dependent hyper-parameters from command lines. Usage: 1) The hyper-parameter will be set as the default value defined in Server.init() if not specifying this term, 2) For algorithms with one or more parameters, use --algo_para v1 v2 ... to specify the values for the parameters. The input order depends on the dict Server.algo_para defined in Server.__init__().

Logger's setting

• logger is used to selected the logger that has the same name with this term.

• log_level shares the same meaning with the LEVEL in the python's native module logging.

• log_file controls whether to store the running-time information into .log in fedtask/taskname/log/, default value is false.

• no_log_console controls whether to show the running time information on the console, and default value is false.

More

To get more information and full-understanding of FLGo please refer to our website.

In the website, we offer :

• API docs: Detailed introduction of packages, classes and methods.
• Tutorial: Materials that help user to master FLGo.

Architecture

We seperate the FL system into five parts:algorithm, benchmark, experiment, simulator and utils.

├─ algorithm
│  ├─ fedavg.py                   //fedavg algorithm
│  ├─ ...
│  ├─ fedasync.py                 //the base class for asynchronous federated algorithms
│  └─ fedbase.py                  //the base class for federated algorithms
├─ benchmark
│  ├─ mnist_classification			//classification on mnist dataset
│  │  ├─ model                   //the corresponding model
│  |  └─ core.py                 //the core supporting for the dataset, and each contains three necessary classes(e.g. TaskGen, TaskReader, TaskCalculator)							
│  ├─ ...
│  ├─ RAW_DATA                   // storing the downloaded raw dataset
│  └─ toolkits						//the basic tools for generating federated dataset
│     ├─ cv                      // common federal division on cv
│     │  ├─ horizontal           // horizontal fedtask
│     │  │  └─ image_classification.py   // the base class for image classification
│     │  └─ ...
│     ├─ ...
│     ├─ base.py                 // the base class for all fedtask
│     ├─ partition.py            // the parttion class for federal division
│     └─ visualization.py        // visualization after the data set is divided
├─ experiment
│  ├─ logger                            //the class that records the experimental process
│  │  ├─ basic_logger.py		    	//the base logger class
│  |  └─ simple_logger.py				//a simple logger class
│  ├─ analyzer.py                  //the class for analyzing and printing experimental results
│  ├─ res_config.yml                  //hyperparameter file of analyzer.py
│  ├─ run_config.yml                  //hyperparameter file of runner.py
|  └─ runner.py                    //the class for generating experimental commands based on hyperparameter combinations and processor scheduling for all experimental 
├─ system_simulator                     //system heterogeneity simulation module
│  ├─ base.py							//the base class for simulate system heterogeneity
│  ├─ default_simulator.py				//the default class for simulate system heterogeneity
|  └─ ...
├─ utils
│  ├─ fflow.py							//option to read, initialize,...
│  └─ fmodule.py						//model-level operators
└─ requirements.txt 

Benchmark

We have added many benchmarks covering several different areas such as CV, NLP, etc

Algorithm

This module is the specific federated learning algorithm implementation. Each method contains two classes: the Server and the Client.

Server

The whole FL system starts with the main.py, which runs server.run() after initialization. Then the server repeat the method iterate() for num_rounds times, which simulates the communication process in FL. In the iterate(), the BaseServer start with sampling clients by select(), and then exchanges model parameters with them by communicate(), and finally aggregate the different models into a new one with aggregate(). Therefore, anyone who wants to customize its own method that specifies some operations on the server-side should rewrite the method iterate() and particular methods mentioned above.

Client

The clients reponse to the server after the server communicate_with() them, who first unpack() the received package and then train the model with their local dataset by train(). After training the model, the clients pack() send package (e.g. parameters, loss, gradient,... ) to the server through reply().

Experiment

The experiment module contains experiment command generation and scheduling operation, which can help FL researchers more conveniently conduct experiments in the field of federated learning.

simulator

The system_simulator module is used to realize the simulation of heterogeneous systems, and we set multiple states such as network speed and availability to better simulate the system heterogeneity of federated learning parties.

Utils

Utils is composed of commonly used operations:

1. model-level operation (we convert model layers and parameters to dictionary type and apply it in the whole FL system).
2. API for the FL workflow like gen_benchmark, gen_task, init, ...

Citation

Please cite our paper in your publications if this code helps your research.

@misc{wang2021federated,
      title={Federated Learning with Fair Averaging}, 
      author={Zheng Wang and Xiaoliang Fan and Jianzhong Qi and Chenglu Wen and Cheng Wang and Rongshan Yu},
      year={2021},
      eprint={2104.14937},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{wang2023flgo,
      title={FLGo: A Fully Customizable Federated Learning Platform}, 
      author={Zheng Wang and Xiaoliang Fan and Zhaopeng Peng and Xueheng Li and Ziqi Yang and Mingkuan Feng and Zhicheng Yang and Xiao Liu and Cheng Wang},
      year={2023},
      eprint={2306.12079},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

Contacts

Zheng Wang, [email protected]

Buy Me a Coffee ☕

Buy me a coffee if you'd like to support the development of this repo.

References

[McMahan. et al., 2017] Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. Communication-Efficient Learning of Deep Networks from Decentralized Data. In International Conference on Artificial Intelligence and Statistics (AISTATS), 2017.

[Cong Xie. et al., 2019] Cong Xie, Sanmi Koyejo, Indranil Gupta. Asynchronous Federated Optimization.

[John Nguyen. et al., 2022] John Nguyen, Kshitiz Malik, Hongyuan Zhan, Ashkan Yousefpour, Michael Rabbat, Mani Malek, Dzmitry Huba. Federated Learning with Buffered Asynchronous Aggregation. In International Conference on Artificial Intelligence and Statistics (AISTATS), 2022.

[Zheng Chai. et al., 2020] Zheng Chai, Ahsan Ali, Syed Zawad, Stacey Truex, Ali Anwar, Nathalie Baracaldo, Yi Zhou, Heiko Ludwig, Feng Yan, Yue Cheng. TiFL: A Tier-based Federated Learning System.In International Symposium on High-Performance Parallel and Distributed Computing(HPDC), 2020

[Mehryar Mohri. et al., 2019] Mehryar Mohri, Gary Sivek, Ananda Theertha Suresh. Agnostic Federated Learning.In International Conference on Machine Learning(ICML), 2019

[Zheng Wang. et al., 2021] Zheng Wang, Xiaoliang Fan, Jianzhong Qi, Chenglu Wen, Cheng Wang, Rongshan Yu. Federated Learning with Fair Averaging. In International Joint Conference on Artificial Intelligence, 2021

[Zeou Hu. et al., 2022] Zeou Hu, Kiarash Shaloudegi, Guojun Zhang, Yaoliang Yu. Federated Learning Meets Multi-objective Optimization. In IEEE Transactions on Network Science and Engineering, 2022

[Tian Li. et al., 2020] Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith. Federated Optimization in Heterogeneous Networks. In Conference on Machine Learning and Systems, 2020

[Xinran Gu. et al., 2021] Xinran Gu, Kaixuan Huang, Jingzhao Zhang, Longbo Huang. Fast Federated Learning in the Presence of Arbitrary Device Unavailability. In Neural Information Processing Systems(NeurIPS), 2021

[Yae Jee Cho. et al., 2020] Yae Jee Cho, Jianyu Wang, Gauri Joshi. Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies.

[Tian Li. et al., 2020] Tian Li, Maziar Sanjabi, Ahmad Beirami, Virginia Smith. Fair Resource Allocation in Federated Learning. In International Conference on Learning Representations, 2020

[Sai Praneeth Karimireddy. et al., 2020] Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, Ananda Theertha Suresh. SCAFFOLD: Stochastic Controlled Averaging for Federated Learning. In International Conference on Machine Learning, 2020