Design Optimization of RF Energy Harvesting Networks for Federated Learning

Federated learning (FL) emerges as a highly pursued approach to machine learning (ML), that enables central model training while maintaining data decentralization and privacy. The distributed computation aspect of FL makes it more appealing for scenarios with constrained bandwidth and energy, which necessitates the usage of more efficient multiple access technologies, such as non-orthogonal multiple access (NOMA), and energy harvesting (EH) technologies, such as wireless power transfer. In this paper, we thus focus on a NOMA-based wireless system with radio frequency (RF) EH for training FL models. The EH users (EHUs) exploit the harvested energy for training their respective local FL models and for transmitting the local model parameters to the base station (BS). Relying on NOMA to reduce latency, we aim at minimizing the duration of a single training round of the common FL model, by jointly optimizing the duration of the EH phase, the durations of the EHUs? uplink transmissions of the EHUs, the EHUs' transmit powers, and the EHUs' processor frequencies. By transforming the resource allocation problem into a convex optimization problem, we have developed a resource allocation scheme with very low computational complexity.