PAUSE: Low-Latency and Privacy-Aware Active User Selection for Federated Learning

• URL: http://arxiv.org/abs/2503.13173v1
• Date: Mon, 17 Mar 2025 13:50:35 GMT
• Title: PAUSE: Low-Latency and Privacy-Aware Active User Selection for Federated Learning
• Authors: Ori Peleg, Natalie Lang, Stefano Rini, Nir Shlezinger, Kobi Cohen,
• Abstract summary: Federated learning (FL) enables edge devices to collaboratively train a machine learning model without the need to share potentially private data.<n>FL poses two key challenges: First, the accumulation of privacy leakage over time, and second, communication latency.<n>We propose a method that jointly addresses the accumulation of privacy leakage and communication latency via active user selection.
• Score: 34.40737362564651
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning (FL) enables multiple edge devices to collaboratively train a machine learning model without the need to share potentially private data. Federated learning proceeds through iterative exchanges of model updates, which pose two key challenges: First, the accumulation of privacy leakage over time, and second, communication latency. These two limitations are typically addressed separately: The former via perturbed updates to enhance privacy and the latter using user selection to mitigate latency - both at the expense of accuracy. In this work, we propose a method that jointly addresses the accumulation of privacy leakage and communication latency via active user selection, aiming to improve the trade-off among privacy, latency, and model performance. To achieve this, we construct a reward function that accounts for these three objectives. Building on this reward, we propose a multi-armed bandit (MAB)-based algorithm, termed Privacy-aware Active User SElection (PAUSE) which dynamically selects a subset of users each round while ensuring bounded overall privacy leakage. We establish a theoretical analysis, systematically showing that the reward growth rate of PAUSE follows that of the best-known rate in MAB literature. To address the complexity overhead of active user selection, we propose a simulated annealing-based relaxation of PAUSE and analyze its ability to approximate the reward-maximizing policy under reduced complexity. We numerically validate the privacy leakage, associated improved latency, and accuracy gains of our methods for the federated training in various scenarios.

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