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pfl-research: simulation framework for accelerating research in Private Federated Learning
April 10, 2024, 4:42 a.m. | Filip Granqvist, Congzheng Song, \'Aine Cahill, Rogier van Dalen, Martin Pelikan, Yi Sheng Chan, Xiaojun Feng, Natarajan Krishnaswami, Vojta Jina, Mon
cs.LG updates on arXiv.org arxiv.org
Abstract: Federated learning (FL) is an emerging machine learning (ML) training paradigm where clients own their data and collaborate to train a global model, without revealing any data to the server and other participants. Researchers commonly perform experiments in a simulation environment to quickly iterate on ideas. However, existing open-source tools do not offer the efficiency required to simulate FL on larger and more realistic FL datasets. We introduce pfl-research, a fast, modular, and easy-to-use Python …
arxiv cs.ai cs.cr cs.cv cs.lg federated learning framework research simulation type
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