Towards Hyper-parameter-free Federated Learning

The adaptive synchronization techniques in federated learning (FL) for scaled global model updates show superior performance over the vanilla federated averaging (FedAvg) scheme. However, existing methods employ additional tunable hyperparameters on the server to determine the scaling factor. A contrasting approach is automated scaling analogous to tuning-free step-size schemes in stochastic gradient descent (SGD) methods, which offer competitive convergence rates and exhibit good empirical performance. In this work, we introduce two algorithms for automated scaling of global model updates. In our first algorithm, we establish that a descent-ensuring step-size regime at the clients ensures descent for the server objective. We show that such a scheme enables linear convergence for strongly convex federated objectives. Our second algorithm shows that the average of objective values of sampled clients is a practical and effective substitute for the objective function value at the server required for computing the scaling factor, whose computation is otherwise not permitted. Our extensive empirical results show that the proposed methods perform at par or better than the popular federated learning algorithms for both convex and non-convex problems. Our work takes a step towards designing hyper-parameter-free federated learning.