Federated Learning (FL) has gained increasing interest in recent years as a distributed on-device learning paradigm. However, multiple challenges remain to be addressed for deploying FL in real-world Internet-of-Things (IoT) networks with hierarchies. Although existing works have proposed various approaches to account data heterogeneity, system heterogeneity, unexpected stragglers and scalibility, none of them provides a systematic solution to address all of the challenges in a hierarchical and unreliable IoT network. In this paper, we propose an asynchronous and hierarchical framework (Async-HFL) for performing FL in a common three-tier IoT network architecture. In response to the largely varied delays, Async-HFL employs asynchronous aggregations at both the gateway and the cloud levels thus avoids long waiting time. To fully unleash the potential of Async-HFL in converging speed under system heterogeneities and stragglers, we design device selection at the gateway level and device-gateway association at the cloud level. Device selection chooses edge devices to trigger local training in real-time while device-gateway association determines the network topology periodically after several cloud epochs, both satisfying bandwidth limitation. We evaluate Async-HFL's convergence speedup using large-scale simulations based on ns-3 and a network topology from NYCMesh. Our results show that Async-HFL converges 1.08-1.31x faster in wall-clock time and saves up to 21.6% total communication cost compared to state-of-the-art asynchronous FL algorithms (with client selection). We further validate Async-HFL on a physical deployment and observe robust convergence under unexpected stragglers.

翻译：联邦学习（FL）近年来作为分布式设备端学习范式受到日益关注。然而，在具有层次结构的真实物联网（IoT）网络中部署FL仍面临多重挑战。尽管现有工作已提出多种方法应对数据异质性、系统异质性、意外掉队者及可扩展性问题，但尚无系统性方案能同时解决分层且不可靠物联网网络中的所有挑战。本文提出一种异步分层框架（Async-HFL），用于在常见三级物联网网络架构中执行FL。针对高度变化的延迟，Async-HFL在网关层与云层均采用异步聚合，从而避免长等待时间。为充分释放Async-HFL在系统异质性和掉队者场景下的收敛速度潜力，我们在网关层设计设备选择机制，在云层设计设备-网关关联机制：设备选择实时触发边缘设备进行本地训练，设备-网关关联则在每若干云轮次后周期性更新网络拓扑，两者均满足带宽限制约束。基于ns-3仿真器及NYCMesh网络拓扑的大规模仿真表明，与现有最先进异步FL算法（含客户端选择）相比，Async-HFL的壁钟时间收敛速度提升1.08-1.31倍，总通信开销降低达21.6%。我们进一步在物理部署中验证Async-HFL，观察到其在意外掉队者下的鲁棒收敛性能。