标题： 一种针对抗毒联邦学习的贝叶斯激励机制 显示英文标题

标题： A Bayesian Incentive Mechanism for Poison-Resilient Federated Learning

摘要： 联邦学习（FL）使得在保护数据隐私的同时，能够在去中心化的客户端之间进行协作模型训练。 然而，其开放参与的特性使其面临数据污染攻击，恶意行为者提交被破坏的模型更新以降低全局模型的性能。 现有的防御措施通常是被动的，依赖于统计聚合规则，这些规则可能计算成本高昂，并且通常假设多数参与者是诚实的。 本文引入了一种主动的经济防御：一种轻量级的贝叶斯激励机制，使恶意行为在经济上变得不理性。 每一轮训练都被建模为一个不完全信息的贝叶斯博弈，在该博弈中，服务器作为委托人，使用一个小的私有验证数据集在发放报酬之前验证更新质量。 该设计满足个体理性（IR），确保善意客户的参与是有利可图的，并且满足激励相容性（IC），使得污染成为一种经济上占优的策略。 在MNIST和FashionMNIST的非独立同分布划分上的大量实验表明了其鲁棒性：在MNIST上有50%的标签翻转攻击时，该机制保持96.7%的准确率，仅比30%标签翻转攻击场景下的准确率低0.3个百分点。 这一结果比标准的FedAvg好51.7个百分点，后者在相同的50%攻击下会崩溃。 该机制计算轻量，预算有限，并且可以轻松集成到现有的FL框架中，为经济上稳健和可持续的FL生态系统提供了一条实用的路径。 显示英文摘要

摘要： Federated learning (FL) enables collaborative model training across decentralized clients while preserving data privacy. However, its open-participation nature exposes it to data-poisoning attacks, in which malicious actors submit corrupted model updates to degrade the global model. Existing defenses are often reactive, relying on statistical aggregation rules that can be computationally expensive and that typically assume an honest majority. This paper introduces a proactive, economic defense: a lightweight Bayesian incentive mechanism that makes malicious behavior economically irrational. Each training round is modeled as a Bayesian game of incomplete information in which the server, acting as the principal, uses a small, private validation dataset to verify update quality before issuing payments. The design satisfies Individual Rationality (IR) for benevolent clients, ensuring their participation is profitable, and Incentive Compatibility (IC), making poisoning an economically dominated strategy. Extensive experiments on non-IID partitions of MNIST and FashionMNIST demonstrate robustness: with 50% label-flipping adversaries on MNIST, the mechanism maintains 96.7% accuracy, only 0.3 percentage points lower than in a scenario with 30% label-flipping adversaries. This outcome is 51.7 percentage points better than standard FedAvg, which collapses under the same 50% attack. The mechanism is computationally light, budget-bounded, and readily integrates into existing FL frameworks, offering a practical route to economically robust and sustainable FL ecosystems.