Joint Scheduling for Federated Learning in Battery-Powered IIoT with Multiple Services
Jianbo Wu, Hao Wu, Shaoshuai Fan, Hui Tian · 2024
Federated learning has been envisioned as a promising technique to enable the intelligence of Industrial Internet of Things (IIoT). An efficient resource management algorithm is critical yet hard to design in IIoT due to limited power devices and the variety of co-existent services. In this paper, we propose a multi-service joint scheduling algorithm for federated learning on IIoT devices to maximize model accuracy by forming a loss function minimization problem under long-term device energy constraints. The problem is then reformulated into a single-round optimization, which can be solved through a binary search and a greedy algorithm. The transmit power of devices, the CPU frequency for model training, and the selection of devices' services are jointly optimized. Simulations demonstrate that our proposed algorithm outperforms the benchmarks in model accuracy, especially when the energy of the device battery is constrained.