Incentive-Driven Federated Learning for Collaborative Agricultural Consumer Electronics

To address the challenges of data privacy protection and collaborative management in agricultural consumer electronics (ACE) devices, this study proposes a distributed data processing framework based on federated learning (FL). The framework employs a three-tier system architecture comprising a base station (BS), edge devices that train models using local data, and a BS that aggregates these models to generate a global one. This iterative process establishes a paradigm that balances privacy preservation and performance optimization. Additionally, we design an incentive mechanism based on auction theory to simulate the buyer-seller interaction between BSs and edge devices. In this mechanism, devices submit bids according to their minimum energy requirements. To maximize resource allocation utility, we propose a greedy auction algorithm that satisfies multiple economic properties. Simulation results demonstrate that the algorithm not only guarantees these properties but also enhances system utility and efficiency.