Communication-Adaptive-Gradient Sparsification for Federated Learning With Error Compensation

Federated learning (FL) has emerged as a popular distributed machine-learning paradigm. It involves many rounds of iterative communication between nodes to exchange model parameters. With the increasing complexity of ML tasks, the models can be large, having millions of parameters. Moreover, edge and IoT nodes often have limited energy resources and channel bandwidths. Thus, reducing the communication cost in FL is a bottleneck problem. This cost could be in terms of energy consumed, delay involved, or amount of data communicated. We propose a communication cost-adaptive model sparsification for FL with error compensation. The central idea is to adapt the sparsification level in run-time by optimizing the ratio between the impact of the communicated model parameters and communication cost. We carry out a detailed convergence analysis to establish the theoretical foundations of the proposed algorithm. We conduct extensive experiments to train both convex and nonconvex machine learning models on a standard dataset. We illustrate the efficiency of the proposed algorithm by comparing its performance with three baseline schemes. The performance of the proposed algorithm is validated for two communication models and three cost functions. Simulation results show that the proposed algorithm needs a substantially less amount of communication than the three baseline schemes while achieving the best accuracy and fastest convergence. The results are consistent for all the considered cost models, cost functions, and ML models. Thus, the proposed <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FL-CATE</monospace> algorithm can substantially improve the communication efficiency of FL, irrespective of the ML tasks, costs, and communication models.