Distributed cooperative learning over time-varying random networks using a gossip-based communication protocol

Motivated by applications of distributed estimation and distributed decision making in wireless sensor networks (WSNs) and unmanned aerial vehicle (UAV) networks, we study a distributed learning problem over time-varying undirected random networks. Using a gossip-based communication protocol, a novel distributed cooperative learning (DCL) algorithm, termed the gossip-based DCL (GBDCL) algorithm, is presented to solve the problem by training the raw data distributed and blocked throughout different nodes. Exploiting the robustness of the gossip-based protocol, each node is guaranteed to build the same learning model in theory against random disconnections and communication route variations in the network topology. It is proved that the GBDCL algorithm converges to the optimal consensus asymptotically. The correctness and effectiveness of the presented GBDCL algorithm are verified in the theoretical analysis and simulations. (C) 2019 Elsevier B.V. All rights reserved.