Federated Learning Framework

FedERA is a highly dynamic and customizable framework that can accommodate many use cases with flexibility by implementing several functionalities over different federated learning algorithms, and essentially creating a plug-and-play architecture to accommodate different use cases.

Supported Devices

FedERA has been extensively tested on and works with the following devices:

• Intel CPUs
• Nvidia GPUs
• Nvidia Jetson
• Raspberry Pi
• Intel NUC

With FedERA, it is possible to operate the server and clients on separate devices or on a single device through various means, such as utilizing different terminals or implementing multiprocessing.

Installation

• Install the latest version from source code:

$ git clone https://github.com/anupamkliv/FedERA.git
$ cd FedERA
$ pip install -r requirements.txt

• Install the stable version (old version) via pip:

# assign the version federa==1.0.0
$ pip install federa

• Using Docker

Create a docker image

docker build -t federa .

Run the docker image

docker run federa

Documentation

Website documentation has been made availbale for FedERA. Please visit FedERA Documentation for more details.

1. Overview
2. Installation
3. Tutorials
4. Contribution
5. API Reference

Starting server

python -m federa.server.start_server \
 --algorithm fedavg \
 --clients 1 \
 --rounds 10 \
 --epochs 10 \
 --batch_size 10 \
 --lr 0.01 \
 --dataset mnist \

Starting client

python -m federa.client.start_client \
 --ip localhost:8214 \
 --device cpu \

Arguments to the clients and server

Server

Argument	Description	Default
algorithm	specifies the aggregation algorithm	fedavg
clients	specifies number of clients selected per round	1
fraction	specifies fraction of clients selected	1
rounds	specifies total number of rounds	1
model_path	specifies initial server model path	initial_model.pt
epochs	specifies client epochs per round	1
accept_conn	determines if connections accepted after FL begins	1
verify	specifies if verification module runs before rounds	0
threshold	specifies minimum verification score	0
timeout	specifies client training time limit per round	None
resize_size	specifies dataset resize dimension	32
batch_size	specifies dataset batch size	32
net	specifies network architecture	LeNet
dataset	specifies dataset name	MNIST
niid	specifies data distribution among clients	1
carbon	specifies if carbon emissions tracked at client side	0
encryption	specifies whether to use ssl encryption or not	0
server_key	specifies path to server key certificate	server-key.pem
server_cert	specifies path to server certificate	server.pem

Client

Argument	Description	Default
server_ip	specifies server IP address	localhost:8214
device	specifies device	cpu
encryption	specifies whether to use ssl encryption or not	0
ca	specifies path to CA certificate	ca.pem
wait_time	specifies time to wait before reconnecting to server	30

Architecture

Files architecture of FedERA. These contents may be helpful for users to understand our repo.

FedERA
├── federa
│   ├── client
│   │   ├── src
│   |   |   ├── client_lib
│   |   |   ├── client
│   |   |   ├── ClientConnection_pb2_grpc
│   |   |   ├── ClientConnection_pb2
│   |   |   ├── data_utils
│   |   |   ├── distribution
│   |   |   ├── get_data
│   |   |   ├── net_lib
│   |   |   ├── net
│   │   └── start_client
│   ├── server
│   │   ├── src
│   │   |   ├── algorithms
│   │   |   ├── server_evaluate
│   │   |   ├── client_connection_servicer
│   │   |   ├── client_manager
│   │   |   ├── client_wrapper
│   │   |   ├── ClientConnection_pb2_grpc
│   │   |   ├── ClientConnection_pb2
│   │   |   ├── server_lib
│   │   |   ├── server
│   │   |   ├── verification
│   │   └── start_server
|   └── test
|       ├── minitest
|       └── misc
│        
└── test
    ├── misc
    ├── benchtest
    |   ├── test_results
    |   └── test_scalability
    └──unittest
        ├── test_algorithms
        ├── test_datasets
        ├── test_models
        └── test_modules
        

The framework is be composed of 4 modules, each module building upon the last:

• Module 1: Verification module docs
• Module 2: Timeout module docs
• Module 3: Intermediate client connections module docs
• Module 4: Carbon emission tracking module docs

Running tests

Various unit tests and bench tests are available in the test directory. To run any tests, run the following command from the root directory:

python -m test.unittest.test_algorithms
python -m test.unittest.test_datasets
python -m test.unittest.test_models
python -m test.unittest.test_modules

Federated Learning Algorithms

Following federated learning algorithms are implemented in this framework:

Method	Paper	Publication
FedAvg	Communication-Efficient Learning of Deep Networks from Decentralized Data	AISTATS'2017
FedDyn	Federated Learning Based on Dynamic Regularization	ICLR' 2021
Scaffold	SCAFFOLD: Stochastic Controlled Averaging for Federated Learning	ICML'2020
Personalized FedAvg	Improving Federated Learning Personalization via Model Agnostic Meta Learning	Pre-print
FedAdagrad	Adaptive Federated Optimization	ICML'2020
FedAdam	Adaptive Federated Optimization	ICML'2020
FedYogi	Adaptive Federated Optimization	ICML'2020
Mime	Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning	ICML'2020
Mimelite	Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning	ICML'2020

Datasets & Data Partition

Sophisticated in the real world, FL needs to handle various kind of data distribution scenarios, including iid and non-iid scenarios. Though there already exists some datasets and partition schemes for published data benchmark, it still can be very messy and hard for researchers to partition datasets according to their specific research problems, and maintain partition results during simulation.

Data Partition

We provide multiple Non-IID data partition schemes. Here we show the data partition visualization of several common used datasets as the examples.

Balanced IID partition

Each client has same number of samples, and same distribution for all class samples.

Non-IID partition 2

Non-IID partition 3

Datasets Supported

Dataset	Training samples	Test samples	Classes
MNIST	60,000	10,000	10
FashionMnist	60,000	10,000	10
CIFAR-10	50,000	10,000	10
CIFAR-100	50,000	10,000	100

Custom Dataset Support

We also provide a simple way to add your own dataset to the framework. The models employed in this framework were trained using a limited subset of the publicly accessible benchmark dataset MedMNIST v2 (link). We specifically selected four different medical image classes from this dataset, which include breast ultrasound (US), chest X-ray, retinal optical coherence tomography (OCT), and tissue microscopy. Each image within the dataset possesses dimensions of 28x28 pixels. For the framework's implementation, we utilized this custom dataset for both the side server and the client. Look into docs for more details.

Models Supported

FedERA has support for the following Deep Learning models, which are loaded from torchvision.models:

• LeNet-5
• ResNet-18
• ResNet-50
• VGG-16
• AlexNet

Custom Model Support

We also provide a simple way to add your own models to the framework. Look into docs for more details.

Carbon emission tracking

In FedERA CodeCarbon package is used to estimate the carbon emissions generated by clients during training. CodeCarbon is a Python package that provides an estimation of the carbon emissions associated with software code.

Performance Evaluation under different Non-IID setting

Plotting the accuracy of some algorithms against different Non-IID distributions

Plotting accuracy on Non-IID distribution with different algorithms

Comparing accuracy of different algorithm with different Non-IID distributions

Contact

For technical issues related to FedERA development, please contact our development team through Github issues or email:

Principal Investigator

Dr Debdoot Sheet Department of Electrical Engineering, Indian Institute of Technology Kharagpur email: [email protected]

Contributor

Anupam Borthakur Centre of Excellence in Artificial Intelligence, Indian Institute of Technology Kharagpur email: [email protected] Github username: anupam-kliv

Asim Manna Centre of Excellence in Artificial Intelligence, Indian Institute of Technology Kharagpur email: [email protected] Github username: asimmanna17

Aditya Kasliwal Manipal Institute of Technology email: [email protected] Github username: Kasliwal17

Dipayan Dewan Centre of Excellence in Artificial Intelligence, Indian Institute of Technology Kharagpur email: [email protected] Github username: dipayandewan94