发表机构
University of Texas at Arlington; Meta Platforms, Inc.; Missouri University of Science and Technology（德克萨斯大学阿灵顿分校; 元平台公司; 密苏里科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
HybridFLow利用SDN全局视图为混合联邦学习生成通信时间估计并分区客户端，平衡延迟与陈旧度，实验显示比SmartFLow快33-40%达到目标准确率。
AI 中文摘要
跨孤岛联邦学习（FL）使地理上分布的机构能够在无需共享原始数据的情况下协作训练机器学习模型。然而，在广域网部署中，通信延迟往往主导轮次完成时间，并加剧掉队者效应。混合联邦学习通过结合同步和异步客户端参与来解决这一挑战，但有效的分区需要洞察网络条件，如共享瓶颈、链路利用率和路径争用，这些是单个客户端无法观察到的。我们提出了HybridFLow，一个闭环的SDN驱动的编排框架，将网络层智能直接集成到混合联邦学习中。利用SDN控制器的全局拓扑视图，HybridFLow在每轮训练之前生成校准的每客户端通信时间估计，并利用这些估计将客户端划分为同步和异步组，同时平衡轮次延迟和更新陈旧度。每轮之后，测量的通信时间被反馈给控制器，以持续改进未来的预测。在多个网络拓扑上的实验结果表明，与SmartFLow相比，HybridFLow达到80%目标准确率的速度快33-40%，并将平均轮次持续时间减少30-40秒，而FedAsync在非独立同分布数据分布下无法达到目标准确率。
英文摘要
Cross-silo Federated Learning (FL) enables geographically distributed institutions to collaboratively train machine learning models without sharing raw data. In wide-area deployments, however, communication delays often dominate round completion time and exacerbate the straggler effect. Hybrid FL addresses this challenge by combining synchronous and asynchronous client participation, but effective partitioning requires visibility into network conditions such as shared bottlenecks, link utilization, and path contention that individual clients cannot observe. We present HybridFLow, a closed-loop SDN-driven orchestration framework that integrates network-layer intelligence directly into hybrid FL. Leveraging the SDN controller's global topology view, HybridFLow generates calibrated per-client communication-time estimates before each training round and uses them to partition clients into synchronous and asynchronous groups while balancing round latency and update staleness. After each round, measured communication times are fed back to the controller to continuously refine future predictions. Experimental results across multiple network topologies show that HybridFLow reaches 80% target accuracy 33-40% faster than SmartFLow and reduces average round duration by 30-40 seconds, while FedAsync fails to reach the target accuracy under non-IID data distributions.
