A Non-parametric View of FedAvg and FedProx:Beyond Stationary Points

Federated Learning (FL) is a promising decentralized learning framework and has great potentials in privacy preservation and in lowering the computation load at the cloud. Recent work showed that FedAvg and FedProx -- the two widely-adopted FL algorithms -- fail to reach the stationary points of the global optimization objective even for homogeneous linear regression problems. Further, it is concerned that the common model learned might not generalize well locally at all in the presence of heterogeneity. In this paper, …

algorithms beyond cloud computation decentralized federated learning framework global non-parametric optimization parametric preservation privacy work