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 F