Learning From Your Neighbours: Mobility-Driven Device-Edge-Cloud Federated Learning

Federated learning (FL) in large-scale wireless networks is implemented in a hierarchical way by introducing edge servers as relays between the cloud server and devices, where devices are dispersed within multiple clusters coordinated by edges. However, the devices are usually mobile users with unpredictable mobile trajectories, whose effects on the model training process are still less studied. In this work, we propose a new MobIlity-Driven feDerated LEarning framework, namely MIDDLE in wireless networks, which can relieve unbalanced and biased model updates by leveraging the new model aggregation opportunities on mobile devices due to their mobility across edges. Specifically, mobile devices can have different models while traversing across edges, and adequately aggregate these models on the device. By theoretical analysis, we can show that this on-device model aggregation can reduce the bias of model updating on edges and cloud, and then accelerate the convergence of model training in FL. Then, we define a model similarity utility to measure the difference in gradient updates among various models, which guides the adaptive on-device model aggregation and inedge device selection to facilitate the comprehensive information sharing between edges. Extensive experiment results validate that MIDDLE can achieve 1.51 x -6.85x speedup on the model training, compared with the state-of-the-art model training approaches in hierarchical FL.