$\texttt{pfl-research}$: simulation framework for accelerating research in Private Federated Learning

Filip Granqvist, Congzheng Song, Áine Cahill, Rogier van Dalen, Martin Pelikan, YI SHENG CHAN, Xiaojun Feng, Natarajan Krishnaswami, Vojta J, Mona Chitnis

Keywords: Federated Learning, Differential Privacy, Fine-tuning LLM

TL;DR: We introduce pfl-research, a fast, modular, and easy-to-use Python framework for simulating (P)FL, 7-72x faster than alternative open-source frameworks, which enables simulation of larger models on larger datasets, e.g. LLMs

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 large and realistic FL datasets.
We introduce $\texttt{pfl-research}$, 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 $\texttt{pfl-research}$ 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.

Submission Number: 10