{"repo":"anupamkliv/FedERA","free":true,"listed":false,"github":"https://github.com/anupamkliv/FedERA","clone":"git clone https://github.com/anupamkliv/FedERA.git","description":"FedERA is a modular and fully customizable open-source FL framework, aiming to address these issues by offering comprehensive support for heterogeneous edge devices and incorporating both standalone and distributed computing. It includes new software modules to enhance usability and promote environ- mental sustainability.","language":"Jupyter Notebook","stars":139,"topics":["carbon-emissions","deep-learning","distributed-machine-learning","edge-devices","federa","federated-learning","framework","grpc","python","pytorch"],"license":"Apache-2.0","category":"machine-learning","readme_excerpt":"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: - Install the stable version (old version) via pip: - Using Docker Create a docker image Run the docker image 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 Starting client 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","default_branch":null,"files":null,"tree":[],"storefront":"/r/anupamkliv","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/anupamkliv/FedERA/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}