Adaptive Modulation for Wireless Federated Learning

In wireless federated learning, the unreliable communication has a significant impact on the convergence rate and learning latency, which cannot be ignored. To cope with this problem, we propose a novel modulation selection mechanism to achieve the balance between learning latency and convergence rate loss caused by stochastic channel error. Different from the traditional one, the modulation mode in wireless FL system should be adjusted according to devices’ computing power, channel conditions, and training data importance. Then, an optimization problem to maximize the learning efficiency is formulated to obtain the optimal modulation scheme. Finally, extensive experiments are implemented to demonstrate the effectiveness of the proposed mechanism.