Energy-Efficient and Adaptive Gradient Sparsification for Federated Learning

Federated learning is an emerging machine-learning technique that trains an algorithm across multiple decentralized edge devices or clients holding local data samples. It involves training local models on local data and uploading model parameters to a server node at regular intervals to generate a global model which is transmitted to all clients. However, edge nodes often have limited energy resources, and hence performing energy-efficient communication of model parameters is a bottleneck problem. We propose an energy-adaptive model sparsification for Federated Learning. The central idea is to adapt the sparsification level in run-time by optimizing the ratio between information content and energy cost. We illustrate the efficiency of the proposed algorithm by comparing its performance with three baseline schemes. We validate the performance of the proposed algorithm for two cost models. Simulation results show that the proposed algorithm needs exponentially less amount of communication and energy as compared to the three baseline schemes while achieving the best accuracy and fastest convergence.