pfl-research: simulation framework for accelerating research in Private Federated Learning
<|reference_start|>pfl-research: simulation framework for accelerating research in Private Federated Learning: 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.<|reference_end|>
@article{granqvist2024pfl-research:,
    title={pfl-research: simulation framework for accelerating research in Private
  Federated Learning},
    author={Filip Granqvist, Congzheng Song, 'Aine Cahill, Rogier van Dalen,
  Martin Pelikan, Yi Sheng Chan, Xiaojun Feng, Natarajan Krishnaswami, Vojta
  Jina, Mona Chitnis},
    journal={arXiv preprint arXiv:2404.06430},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.06430},
    primaryClass={cs.LG cs.AI cs.CR cs.CV}
}