GTG-CoL: A New Decentralized Federated Learning Based on Consensus for Dynamic Networks

Abstract

One of the main lines of research in distributed learning in the last years is the one related to Federated Learning (FL). In this work, a decentralized Federated Learning algorithm based on consensus (CoL) is applied to Wireless Ad-hoc Networks (WANET), where the agents communicate with other agents to share their learning model as they are available to the range of the wireless connection. When deploying a set of agents is very important to study previous to the deployment if all the agents in the WANET will be reachable. The paper proposes to study it by generating a simulation close to the real world using a framework that allows the easy development and modification of simulations based on Unity and SPADE agents. A fruit orchard with autonomous tractors is presented as a case study.