Device Scheduling and Assignment in Hierarchical Federated Learning for Internet of Things

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 …

aggregation challenge communication congestion cs.dc cs.lg devices edge edge servers federated learning hierarchical internet internet of things iot issue machine machine learning multiple network population scheduling servers