Federated Learning on Heterogeneous Opportunistic Networks
Yuchen Deng, Yan Xin · 2024
Opportunistic learning plays a crucial role in heterogeneous opportunity networks. Federated learning enables nodes to learn new knowledge from models of other nodes, facilitating opportunistic learning in heterogeneous opportunity networks. However, applying federated learning to heterogeneous opportunity networks faces challenges such as slow convergence rates in large-scale networks, lack of training efficiency, node heterogeneity, and data heterogeneity. In this paper, we propose a distributed opportunistic federated learning model designed for large-scale networks, providing a multi-node grouping protocol. We present the convergence proof of the opportunistic federated learning algorithm. Additionally, we optimize the transfer matrix of opportunistic federated learning for addressing data heterogeneity and network heterogeneity shifts.