发表机构
Deakin University; Applied Artificial Intelligence Initiative（迪肯大学; 应用人工智能倡议）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对异构环境下联邦学习的分布不匹配问题，提出SAPE-FL框架，通过双锚定机制平衡全局与对等知识，在高异构低数据场景下性能优于现有方法。
AI 中文摘要
联邦学习（FL）允许分散的客户端在保护数据隐私的前提下协同训练模型。然而，客户端间的分布不匹配往往会导致全局泛化能力差，客户端层面的本地性能下降。在这种场景下，部分仅用本地数据训练本地模型的客户端可能表现优于全局学习到的模型，从而抵消了联邦协同学习的优势。为解决该问题，我们提出SAPE-FL（相似度感知个性化联邦学习，Similarity-Aware Personalized Federated Learning），这是一种新型个性化框架，将每个客户端的模型锚定到全局模型和相似度加权的对等平均模型。通过结合基于模型相似度和输出相似度的动态、客户端特定正则化项，SAPE-FL自适应平衡全局知识迁移与对等协作，同时过滤掉不相似的客户端。这种双锚定机制缓解了负迁移，增强了异构环境下的鲁棒性。我们对该算法进行了理论分析，确立了其收敛性保证，并通过实验表明，在高统计异构性和低客户端数据场景下，SAPE-FL的性能优于当前最优方法。
英文摘要
Federated Learning (FL) allows decentralized clients to train models collaboratively while preserving data privacy. However, distribution mismatch across clients often leads to poor global generalization and degraded local client-level performance. In such scenarios, some of the clients with their local models trained solely on local data may perform better than the globally learnt model, thus nullifying the benefits of collaborative federated learning. To address this, we propose SAPE-FL (Similarity-Aware Personalized Federated Learning), a novel personalization framework that anchors each client's model to both the global model and a similarity-weighted peer averaged model. By incorporating dynamic, client-specific regularization based on both model similarity and output similarity, SAPE-FL adaptively balances global knowledge transfer and peer collaboration while filtering out dissimilar clients. This dual anchoring mitigates negative transfer and enhances robustness in heterogeneous settings. We theoretically analyze our algorithm establishing its convergence guarantees and empirically show that SAPE-FL outperforms state-of-the-art methods under high statistical heterogeneity and low client data regimes.
