Incentive-Driven Federated Learning for Collaborative Agricultural Consumer Electronics

This is the authors' abstract. We don't add key points for this paper.

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.