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Peer-Relative Trust-Guided Update Screening for Robust Semi-Decentralized Federated Learning under Label-Flipping Attacks

Ajay Kumar Shrestha, Sandhya Joshi · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.25714 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

信任和声誉核心机制可以减少恶意客户在联邦学习(FL)中的影响力,但严格的更新过滤在非独立和相同分布的(非IID)数据下是困难的,因为诚实的本地更新可能会暂时降低共享验证代理的性能。 基于我们以前的信任为基础的半分散影响FL(SDFL)框架,这篇论文可操作信任信号,并引入基于对等的随机更新筛选,该方法结合了强劲的验证与实用性评分,以方向和基于规范的更新质量。 该方法被评估了50个MNIST客户,20%持久的标签-flip攻击者,Dirichlet的异质化水平为0.5和0.1、100个通信环节,以及五个配对

Machine translation (Meta M2M100); the English original follows.

Abstract

Trust and reputation mechanisms can reduce the influence of malicious clients in federated learning (FL), but strict update filtering is difficult under non-independent and identically distributed (non-IID) data because honest local updates may temporarily reduce performance on a shared validation proxy. Building on our earlier trust-based semi-decentralized FL (SDFL) framework, this paper operationalizes the trust signals and introduces peer-relative update screening that combines a robust validation-utility score with direction- and norm-based update quality. The method is evaluated with 50 MNIST clients, 20% persistent label-flip attackers, Dirichlet heterogeneity levels of 0.5 and 0.1, 100 communication rounds, and five paired random seeds. Under moderate heterogeneity, the proposed method reaches 98.37% final accuracy while rejecting 95.82% of malicious updates and retaining 90.30% of benign updates; the final benign and malicious trust distributions are completely separated (KS=1.00). Under severe heterogeneity, it reaches 95.25% accuracy, rejects 88.55% of malicious updates, and retains 76.90% of benign updates (KS=0.96). Trust weighting alone preserves slightly higher utility in the most heterogeneous condition, but accepts all malicious submis-sions. The results therefore distinguish influence mitigation from explicit containment and quantify the utility-selectivity trade-off introduced by screening. The study provides an empirically validated off-chain governance core that can feed blockchain-based audit and incentive layers in SDFL.

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Topics: Computer science, Weighting, Reputation, Federated learning, Independent and identically distributed random variables, Audit

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