Decentralized Federated Learning Using the Metropolis-Hastings for Highly Dynamic UAVs

Unmanned Aerial Vehicles (UAVs) are increasingly employed in cooperative surveillance missions where data collection across disparate areas is crucial. In such systems, data from all UAVs is collected and processed in a central server, making it vulnerable to breaches and unauthorized access. Federated Learning (FL) addresses these concerns by enabling collaborative model training without centralized data collection. In FL, each UAV trains a local model on its own data and only shares the model updates with a central server. The central server then aggregates these parameters to update a global model, which is redistributed to all participating UAVs. However, FL’s reliance on a central server introduces challenges, especially in geographically highly dynamic and dispersed scenarios. The central server can become a single point of failure and may struggle with the communication overhead and latency issues inherent in such dynamic environments. To overcome these limitations, this paper proposes a decentralized federated learning framework for multi-agent UAV systems. This approach facilitates direct sharing of local deep learning (DL) model parameters among UAVs, eliminating the need for a central server. Our approach employs the Metropolis-Hastings algorithm to ensure UAVs achieve consensus on shared model parameters, ensuring balanced weight distribution and stable training processes. We validate our fully distributed DL model aggregation using the ResNet-18 model. Our results confirm DFL’s effectiveness in achieving low RMSE values and rapid convergence, comparable to centralized FL, across dynamic UAV networks.