Decentralized Federated Learning: Model Update Tracking Under Imperfect Information Sharing

arXiv:2403.13247v1 Announce Type: new
Abstract: A novel Decentralized Noisy Model Update Tracking Federated Learning algorithm (FedNMUT) is proposed, which is tailored to function efficiently in the presence of noisy communication channels that reflect imperfect information exchange. This algorithm uses gradient tracking to minimize the impact of data heterogeneity while minimizing communication overhead. The proposed algorithm incorporates noise into its parameters to mimic the conditions of noisy communication channels, thereby enabling consensus among clients through a communication graph topology in such …

abstract algorithm arxiv channels communication cs.dc cs.lg data decentralized federated learning function gradient impact information novel tracking type update