Federated Learning (FL) is a promising machine learning approach for Internet
of Things (IoT), but it has to address network congestion problems when the
population of IoT devices grows. Hierarchical FL (HFL) alleviates this issue by
distributing model aggregation to multiple edge servers. Nevertheless, the
challenge of communication overhead remains, especially in scenarios where all
IoT devices simultaneously join the training process. For scalability,
practical HFL schemes select a subset of IoT devices to participate in the
training, hence the notion of device scheduling. In this setting, only selected
IoT devices are scheduled to participate in the global training, with each of
them being assigned to one edge server. Existing HFL assignment methods are
primarily based on search mechanisms, which suffer from high latency in finding
the optimal assignment. This paper proposes an improved K-Center algorithm for
device scheduling and introduces a deep reinforcement learning-based approach
for assigning IoT devices to edge servers. Experiments show that scheduling 50%
of IoT devices is generally adequate for achieving convergence in HFL with much
lower time delay and energy consumption. In cases where reduction in energy
consumption (such as in Green AI) and reduction of messages (to avoid burst
traffic) are key objectives, scheduling 30% IoT devices allows a substantial
reduction in energy and messages with similar model accuracy.

提出了改进的 K-Center 算法用于设备调度，并引入了基于深度强化学习的方法来分配物联网设备给边缘服务器，证明了在进行 Hierarchical FL 时，调度 50% 的物联网设备通常足以实现收敛，同时延迟和能耗明显降低，而在追求绿色人工智能以及减少消息传输方面，调度 30% 的物联网设备能够在保持模型准确度的同时大幅减少能耗和消息量。