mlsanityData to 14 Oct 2026

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

arXiv 2404.06430 ↗First posted 9 Apr 2024cs.LG cs.AI cs.CRcited by 8

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Abstract (arXiv)

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× 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.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 59 of its 88 references that are in the map. First posted on arXiv 9 Apr 2024.

Builds on

59 of 88 references.

References · 59

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38 more
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  17. 2021When the Curious Abandon Honesty: Federated Learning Is Not Privatecited by 127
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  28. 2023(Amplified) Banded Matrix Factorization: A unified approach to private trainingcited by 49
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50 most cited of 59 All 59 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 5; 2026: 1.

8 papers.

  1. 2024Banded Square Root Matrix Factorization for Differentially Private Model Trainingcited by 12
  2. 2024Improved Modelling of Federated Datasets using Mixtures-of-Dirichlet-Multinomialscited by 3
  3. 2025FedRS-Bench: Realistic Federated Learning Datasets and Benchmarks in Remote Sensingcited by 3
  4. 2025OmniFed: A Modular Framework for Configurable Federated Learning from Edge to HPCcited by 1
  5. 2025Correlating Cross-Iteration Noise for DP-SGD using Model Curvaturecited by 1
  6. 2025Cocoon: A System Architecture for Differentially Private Training with Correlated Noisescited by 1
  7. 2025Decentralized Federated Learning of Probabilistic Generative Classifierscited by 0
  8. 2026SoK: Enhancing Cryptographic Collaborative Learning with Differential Privacycited by 0