pfl-research: simulation framework for accelerating research in Private Federated Learning
Fuente:
arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866913605148999680 |
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| author | Granqvist, Filip Song, Congzheng Cahill, Áine van Dalen, Rogier Pelikan, Martin Chan, Yi Sheng Feng, Xiaojun Krishnaswami, Natarajan Jina, Vojta Chitnis, Mona |
| author_facet | Granqvist, Filip Song, Congzheng Cahill, Áine van Dalen, Rogier Pelikan, Martin Chan, Yi Sheng Feng, Xiaojun Krishnaswami, Natarajan Jina, Vojta Chitnis, Mona |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_06430 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | pfl-research: simulation framework for accelerating research in Private Federated Learning Granqvist, Filip Song, Congzheng Cahill, Áine van Dalen, Rogier Pelikan, Martin Chan, Yi Sheng Feng, Xiaojun Krishnaswami, Natarajan Jina, Vojta Chitnis, Mona Machine Learning Artificial Intelligence Cryptography and Security Computer Vision and Pattern Recognition 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. |
| title | pfl-research: simulation framework for accelerating research in Private Federated Learning |
| topic | Machine Learning Artificial Intelligence Cryptography and Security Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2404.06430 |