pfl-research: simulation framework for accelerating research in Private Federated Learning

• URL: http://arxiv.org/abs/2404.06430v1
• Date: Tue, 9 Apr 2024 16:23:01 GMT
• Title: pfl-research: simulation framework for accelerating research in Private Federated Learning
• Authors: Filip Granqvist, Congzheng Song, Áine Cahill, Rogier van Dalen, Martin Pelikan, Yi Sheng Chan, Xiaojun Feng, Natarajan Krishnaswami, Vojta Jina, Mona Chitnis,
• Abstract summary: pfl-research is a fast, modular, and easy-to-use Python framework for simulating Federated learning (FL) It supports setups, PyTorch, and non-neural network models, and is tightly integrated with state-of-the-art algorithms. We release a suite of benchmarks that evaluates an algorithm's overall performance on a diverse set of realistic scenarios.
• Score: 6.421821657238535
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 framework for simulating FL. It supports TensorFlow, PyTorch, and non-neural network models, and is tightly integrated with state-of-the-art privacy algorithms. We study the speed of open-source FL frameworks and show that pfl-research is 7-72$\times$ faster than alternative open-source frameworks on common cross-device setups. Such speedup will significantly boost the productivity of the FL research community and enable testing hypotheses on realistic FL datasets that were previously too resource intensive. We release a suite of benchmarks that evaluates an algorithm's overall performance on a diverse set of realistic scenarios. The code is available on GitHub at https://github.com/apple/pfl-research.

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