Energy Aware Analog Aggregation for Federated Learning with Redundant Data
Federated learning (FL) enables workers to learn a model collaboratively by using their local data, with the help of a parameter server (PS) for global model aggregation. The high communication cost for periodic model updates and the nonindependent and identically distributed (i.i.d. data become major bottlenecks for FL. In this work, we consider analog aggregation to scale down the communication cost with respect to the number of workers, and introduce data redundancy to the system to deal with non i.i.d. data. We propose an online energy aware dynamic worker scheduling policy, which maximizes the average number of workers scheduled for gradient update at each iteration under a long term energy constraint, and analyze its performance based on Lyapunov optimization. Experiments using MNIST dataset show that, for non i.i.d. data, doubling data storage can improve the accuracy by 9.8% under a stringent energy budget, while the proposed policy can achieve close to optimal accuracy without violating the energy constraint.
ICC 2020 2020 IEEE International Conference on Communications (ICC)