Wireless Decentralized Federated Learning with Energy-Constrained Clients

Machine Learning (ML) is a critical tool for data-driven classification and regression tasks. With the increasing availability of computationally powerful wireless edge devices, distributed ML has been in the spotlight in recent years for largescale wireless applications. In that domain, Federated Learning (FL) is attractive as a means to preserve data privacy while enabling joint model training amongst multiple clients with their own private data. However, traditional FL has limitations such as a single point of failure, high communication overhead and uncertain trustworthiness of both clients and aggregator. Decentralized FL methods have been proposed to address these concerns by enabling each client to aggregate model updates from other clients. In this paper, we investigate decentralized FL in a wireless system, taking into account the transmission energy budget of each client which fundamentally limits the range and bandwidth of data communications. To do so, we propose a model partitioning method that highlights the design choices available within the energy constraint - sharing of a larger partition of the model among clients requires transmission range to shrink and therefore sharing with fewer neighboring nodes. It is non-obvious what the best setting of partition size/transmission range is, and we demonstrate that such a setting can be found for particular deployments. We further conducted simulations to validate the method we proposed, and the results demonstrated the practicality of our method in distributed systems operating within an energy budget.