Unit-Modulus Wireless Federated Learning Via Penalty Alternating Minimization
This work addresses communication efficiency for wireless federated learning systems, though it appears incremental as it builds on existing FL methods with a focus on hardware-friendly optimization.
The paper tackles the problem of communication delays and costs in wireless federated learning by proposing a unit-modulus framework that uses optimized phase shifting to upload local and compute global model parameters, achieving smaller training losses and testing errors than benchmarks in CARLA experiments.
Wireless federated learning (FL) is an emerging machine learning paradigm that trains a global parametric model from distributed datasets via wireless communications. This paper proposes a unit-modulus wireless FL (UMWFL) framework, which simultaneously uploads local model parameters and computes global model parameters via optimized phase shifting. The proposed framework avoids sophisticated baseband signal processing, leading to both low communication delays and implementation costs. A training loss bound is derived and a penalty alternating minimization (PAM) algorithm is proposed to minimize the nonconvex nonsmooth loss bound. Experimental results in the Car Learning to Act (CARLA) platform show that the proposed UMWFL framework with PAM algorithm achieves smaller training losses and testing errors than those of the benchmark scheme.