Flight让联邦学习支持复杂层级结构，通信效率提升60%以上。
Flight: A FaaS-Based Framework for Complex and Hierarchical Federated Learning
- 支持多层异构网络，控制与数据分离，可扩展性强。
- 实测支持2048个设备并发，训练时长显著缩短。
- 适合物联网等复杂网络场景的联邦学习部署。
联邦学习（FL）是一种去中心化机器学习范式，模型在分布式设备上训练并由中心服务器聚合。现有框架假设设备直接连接聚合服务器，仅适用于简单两层拓扑，未能利用真实分布式系统（如物联网）的固有层次结构。本文提出Flight，一种支持复杂分层多级拓扑、异步聚合且将控制平面与数据平面解耦的新型联邦学习框架。与当前最先进的Flower框架对比，实验表明Flight可支持高达2048个并发设备，显著降低多种模型的联邦学习总时长，并使通信开销减少超过60%。
原文摘要 · Abstract (English)
Federated Learning (FL) is a decentralized machine learning paradigm where models are trained on distributed devices and are aggregated at a central server. Existing FL frameworks assume simple two-tier network topologies where end devices are directly connected to the aggregation server. While this is a practical mental model, it does not exploit the inherent topology of real-world distributed systems like the Internet-of-Things. We present Flight, a novel FL framework that supports complex hierarchical multi-tier topologies, asynchronous aggregation, and decouples the control plane from the data plane. We compare the performance of Flight against Flower, a state-of-the-art FL framework. Our results show that Flight scales beyond Flower, supporting up to 2048 simultaneous devices, and reduces FL makespan across several models. Finally, we show that Flight's hierarchical FL model can reduce communication overheads by more than 60%.
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