Byzantine-Resilient Over-the-Air Federated Learning under Zero-Trust Architecture
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Abstract (arXiv)
Over-the-air computation (AirComp) has emerged as an essential approach for enabling communication-efficient federated learning (FL) over wireless networks. Nonetheless, the inherent analog transmission mechanism in AirComp-based FL (AirFL) intensifies challenges posed by potential Byzantine attacks. In this paper, we propose a novel Byzantine-robust FL paradigm for over-the-air transmissions, referred to as federated learning with secure adaptive clustering (FedSAC). FedSAC aims to protect a portion of the devices from attacks through zero trust architecture (ZTA) based Byzantine identification and adaptive device clustering. By conducting a one-step convergence analysis, we theoretically characterize the convergence behavior with different device clustering mechanisms and uneven aggregation weighting factors for each device. Building upon our analytical results, we formulate a joint optimization problem for the clustering and weighting factors in each communication round. To facilitate the targeted optimization, we propose a dynamic Byzantine identification method using historical reputation based on ZTA. Furthermore, we introduce a sequential clustering method, transforming the joint optimization into a weighting optimization problem without sacrificing the optimality. To optimize the weighting, we capitalize on the penalty convex-concave procedure (P-CCP) to obtain a stationary solution. Numerical results substantiate the superiority of the proposed FedSAC over existing methods in terms of both test accuracy and convergence rate.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 28 of its 54 references that are in the map. First posted on arXiv 24 Mar 2025.
Builds on
28 of 54 references.
References · 28
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16 more
- 2021Optimized Power Control Design for Over-the-Air Federated Edge Learningcited by 48
- 2021Challenges and Approaches for Mitigating Byzantine Attacks in Federated Learningcited by 47
- 2022Joint Device Selection and Power Control for Wireless Federated Learningcited by 20
- 2022Gradient and Channel Aware Dynamic Scheduling for Over-the-Air Computation in Federated Edge Learning Systemscited by 20
- 2023Channel and Gradient-Importance Aware Device Scheduling for Over-the-Air Federated Learningcited by 9
- 2021BEV-SGD: Best Effort Voting SGD for Analog Aggregation Based Federated Learning against Byzantine Attackerscited by 6
- 2021Robust Federated Learning via Over-The-Air Computationcited by 6
- 2023Byzantine-Resilient Federated Learning at Edgecited by 6
- 2023Imperfect CSI: A Key Factor of Uncertainty to Over-the-Air Federated Learningcited by 6
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- 2022Over-The-Air Federated Learning under Byzantine Attackscited by 4
- 2025Combating Interference for Over-the-Air Federated Learning: A Statistical Approach via RIScited by 4
- 2022Zero Trust Architecture for 6G Securitycited by 3
- 2024Empowering Over-the-Air Personalized Federated Learning via RIScited by 3
- 2023Robust Beamforming Design for RIS-aided Cell-free Systems with CSI Uncertainties and Capacity-limited Backhaulcited by 2
- 2023One-Bit Byzantine-Tolerant Distributed Learning via Over-the-Air Computationcited by 1
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