FedTest通过用户互测模型提升联邦学习收敛速度与抗攻击能力
Federated Testing (FedTest): A New Scheme to Enhance Convergence and Mitigate Adversarial Attacks in Federating Learning
- 用户用本地数据测试其他用户模型，生成可信评分
- 实验显示收敛更快，恶意模型影响显著降低
- 适合注重安全性和效率的分布式学习场景
联邦学习（FL）因其保护数据隐私和高效利用分布式计算资源而成为重要范式，通过在分布式用户间并行训练实现。然而传统方法在评估接收模型质量、处理模型不平衡以及减少有害模型影响方面面临挑战。为此，本文提出一种新型联邦学习框架——联邦测试（FedTest）。在该方法中，特定用户的本地数据用于训练自身模型，并测试其他用户的模型，使各用户可互相评估模型质量，生成准确评分，进而实现高效聚合并识别恶意模型。数值结果表明，所提方法不仅加速了收敛速率，还显著降低了恶意用户的影响，大幅提升联邦学习系统的整体效率与鲁棒性。
原文摘要 · Abstract (English)
Federated Learning (FL) has emerged as a significant paradigm for training machine learning models. This is due to its data-privacy-preserving property and its efficient exploitation of distributed computational resources. This is achieved by conducting the training process in parallel at distributed users. However, traditional FL strategies grapple with difficulties in evaluating the quality of received models, handling unbalanced models, and reducing the impact of detrimental models. To resolve these problems, we introduce a novel federated learning framework, which we call federated testing for federated learning (FedTest). In the FedTest method, the local data of a specific user is used to train the model of that user and test the models of the other users. This approach enables users to test each other's models and determine an accurate score for each. This score can then be used to aggregate the models efficiently and identify any malicious ones. Our numerical results reveal that the proposed method not only accelerates convergence rates but also diminishes the potential influence of malicious users. This significantly enhances the overall efficiency and robustness of FL systems.
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