ePoW Energy-Efficient Blockchain Consensus Algorithm for Decentralize Federated Learning System in Resource-Constrained UAV Swarm

With the proliferation of Unmanned Aerial Vehicles (UAV) and UAV swarms, there has been growing interest in using them for collaborative computing tasks. Blockchain-based Federated learning (BFL) is an excellent approach for training artificial intelligence models in UAV swarms, providing benefits such as privacy protection, trusted computing, node autonomy, and low communication overhead. However, due to limited battery life and processing power, UAVs require energy-efficient solutions that can handle blockchain consensuses in decentralized BFL manners. To address such challenges, we propose an energy-efficient Proof of Work (ePoW) consensus algorithm for the resource-constrained UAV swarms, which to achieve fault tolerance and data integrity while minimizing energy consumption. At the commencement of the ePoW protocol, the UAV nodes perform computation and dissemination of their engagement metrics to facilitate the selection of participating nodes in the ePoW competition. Following a successful BFL global generation event, the dynamic difficulty adjustment mechanism collaborates with the engagement metrics to identify the most reliable node and mitigate resource consumption during the consensus process. Our ePow algorithm is designed to work with BFL systems in a UAV swarm, where each UAV device acts as a node in the BFL system. We evaluate the algorithm’s performance in various scenarios and found that it achieves high accuracy while consuming significantly less energy compared to existing consensus algorithms. Our proposed approach presents a promising solution for energy-efficient decentralized BFL systems in UAV swarms.