Abstract: Methods and systems for federated learning using a parameterized optimization algorithm are described. A central server receives, from each of a plurality of user devices, a proximal map and feedback representing a current state of each user device. The server computes an update to optimization parameters of a parameterized optimization algorithm, using the received feedback. Model updates are computed for each user device, using the received proximal maps and the parameterized optimization algorithm having the updated optimization parameters. Each model update is transmitted to each respective client for updating the respective model.

Abstract: Methods and apparatuses for implementing federated learning are described. A set of updates is obtained, where each update represents a respective difference between a global model and a respective local model. The global model is updated using a weighted average of the set of updates. A set of weighting coefficients is calculated, to be used in calculating the weighted average. The set of weighting coefficients is calculated by performing multi-objective optimization towards a Pareto-stationary solution across the set of updates. The weighted average is calculated by applying the set of weighting coefficients to the set of updates, and the global model is updated by adding the weighted average to the global model.