Enhancing Privacy in Federated Learning through Local Training

arXiv:2403.17572v1 Announce Type: new
Abstract: In this paper we propose the federated private local training algorithm (Fed-PLT) for federated learning, to overcome the challenges of (i) expensive communications and (ii) privacy preservation. We address (i) by allowing for both partial participation and local training, which significantly reduce the number of communication rounds between the central coordinator and computing agents. The algorithm matches the state of the art in the sense that the use of local training demonstrably does not impact …

abstract algorithm arxiv challenges communication communications cs.lg fed federated learning math.oc paper preservation privacy reduce through training type