Detect \& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning

• URL: http://arxiv.org/abs/2506.23583v1
• Date: Mon, 30 Jun 2025 07:40:18 GMT
• Title: Detect \& Score: Privacy-Preserving Misbehaviour Detection and Contribution Evaluation in Federated Learning
• Authors: Marvin Xhemrishi, Alexandre Graell i Amat, Balázs Pejó,
• Abstract summary: Federated learning with secure aggregation enables private and collaborative learning from decentralised data without leaking sensitive client information.<n>QI and FedGT were proposed for contribution evaluation (CE) and misbehaviour detection (MD), respectively.<n>We combine the strengths of QI and FedGT to achieve both robust MD and accurate CE.
• Score: 57.35282510032077
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Federated learning with secure aggregation enables private and collaborative learning from decentralised data without leaking sensitive client information. However, secure aggregation also complicates the detection of malicious client behaviour and the evaluation of individual client contributions to the learning. To address these challenges, QI (Pejo et al.) and FedGT (Xhemrishi et al.) were proposed for contribution evaluation (CE) and misbehaviour detection (MD), respectively. QI, however, lacks adequate MD accuracy due to its reliance on the random selection of clients in each training round, while FedGT lacks the CE ability. In this work, we combine the strengths of QI and FedGT to achieve both robust MD and accurate CE. Our experiments demonstrate superior performance compared to using either method independently.

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