将联邦学习模型迁移至边缘基站，减少通信开销。
EdgeFLow: Serverless Federated Learning via Sequential Model Migration in Edge Networks
- 用边缘基站顺序迁移模型替代云端聚合
- 在非凸和非独立同分布数据下收敛性有保障
- 适合低带宽物联网场景的高效联邦学习
联邦学习（FL）作为物联网时代分布式学习的变革范式，重塑了数据处理方式。然而，客户端与服务器间不可避免的数据交换和远距离传输导致显著通信瓶颈。本文提出EdgeFLow，一种创新的联邦学习框架，通过将传统云服务器替换为边缘基站在边缘集群间的模型顺序迁移，实现仅在边缘集群内进行模型聚合与传播，彻底消除基于云端的传输，大幅降低全局通信开销。我们在非凸目标函数和非独立同分布（non-IID）数据条件下对EdgeFLow进行了严格的收敛性分析，扩展了经典联邦学习理论。多种配置下的实验结果验证了理论分析，表明EdgeFLow在保持相近准确率提升的同时，显著降低了通信成本。作为通信高效联邦学习的系统级架构创新，EdgeFLow为未来物联网和边缘网络学习系统的发展奠定了基础。
原文摘要 · Abstract (English)
Federated Learning (FL) has emerged as a transformative distributed learning paradigm in the era of Internet of Things (IoT), reconceptualizing data processing methodologies. However, FL systems face significant communication bottlenecks due to inevitable client-server data exchanges and long-distance transmissions. This work presents EdgeFLow, an innovative FL framework that redesigns the system topology by replacing traditional cloud servers with sequential model migration between edge base stations. By conducting model aggregation and propagation exclusively at edge clusters, EdgeFLow eliminates cloud-based transmissions and substantially reduces global communication overhead. We provide rigorous convergence analysis for EdgeFLow under non-convex objectives and non-IID data distributions, extending classical FL convergence theory. Experimental results across various configurations validate the theoretical analysis, demonstrating that EdgeFLow achieves comparable accuracy improvements while significantly reducing communication costs. As a systemic architectural innovation for communication-efficient FL, EdgeFLow establishes a foundational framework for future developments in IoT and edge-network learning systems.
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