Robin: An Efficient Hierarchical Federated Learning Framework via a Learning-Based Synchronization Scheme

Abstract

Hierarchical federated learning (HFL) extends traditional federated learning by introducing a cloud-edge-device framework to enhance scalability. However, the challenge of determining when devices and edges should aggregate models remains unresolved, making the design of an effective synchronization scheme crucial. Additionally, the heterogeneity in computing and communication capabilities, coupled with non-independent and identically distributed (non-IID) data distributions, makes synchronization particularly complex. In this article, we propose Robin, a learning-based synchronization scheme for HFL systems. By collecting data such as models’ parameters, CPU usage, communication time, etc., we design a deep reinforcement learning-based approach to decide the frequencies of cloud aggregation and edge aggregation, respectively. The proposed scheme well considers device heterogeneity, non-IID data and device mobility, to maximize the training model accuracy while minimizing the energy overhead. Meanwhile, we prove the convergence of Robin’s synchronization scheme. And we build an HFL testbed and conduct the experiments with real data obtained from Raspberry Pi and Alibaba Cloud. Extensive experiments under various settings are conducted to confirm the effectiveness of Robin, which can improve 31.2% in model accuracy while reducing energy consumption by 36.4%.