Adaptive Federated Learning for Battery-powered IIoT Devices with Non-IID Data

In this paper, we propose a multi-dimensional resource management scheme for Federated Learning with non-independent and identically distributed (non-IID) data on battery-powered IIoT devices. Firstly, we formulate an optimization problem that aims to maximize the learning efficiency given long-term energy and time constraints to balance training accuracy and learning latency. Secondly, based on the derived lower bound of expected convergence rate with non-IID data, we solve the short-term problems by cyclically manage the resources (i.e., radio, computation and resource block (RB) resources, and device energy). Simulation results validate that the proposed scheme outperforms other baseline schemes, especially in energy shortage scenarios.