发表机构
Inha University; University of Toronto; University of Birmingham（仁荷大学; 多伦多大学; 伯明翰大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种潜在信息共享方案，通过共享少量隐藏层激活值缓解客户端漂移，在保持收敛与隐私的同时提升联邦学习训练效率与精度。
AI 中文摘要
联邦学习（FL）是一种通信高效的分布式学习范式。然而，客户端漂移仍然是最关键的挑战之一，阻碍了全局模型的高效训练。在本研究中，我们提出了一种新颖的潜在信息共享方案，直接缓解客户端之间的数据异质性。我们的理论和实证结果表明，共享少量隐藏层激活值能显著提高训练效率，同时保持收敛保证和数据隐私。此外，我们将我们的方法与现有的旨在解决客户端漂移的FL方法（包括FedProx、SCAFFOLD、FedPVR、FedProto和SplitFed）进行比较，并证明在固定的轮次预算下，我们的方法在不产生过多通信开销的情况下实现了更优的模型精度。总体而言，这项工作提出了一种有前景的新知识聚合方案，并对激活共享对联邦优化的影响进行了全面分析。
英文摘要
Federated learning (FL) is a communication-efficient distributed learning paradigm. However, client drift remains one of the most critical challenges, hindering the efficient training of a global model. In this study, we propose a novel latent information sharing scheme that directly mitigates data heterogeneity across clients. Our theoretical and empirical results show that sharing a small amount of hidden-layer activations significantly improves training efficiency while preserving convergence guarantees and data privacy. Furthermore, we compare our method with existing FL approaches designed to address client drift, including FedProx, SCAFFOLD, FedPVR, FedProto, and SplitFed, and demonstrate superior model accuracy under a fixed round budget without incurring excessive communication overhead. Overall, this work presents a promising new knowledge aggregation scheme and provides a comprehensive analysis of the impact of activation sharing on federated optimization.
