DriveFL: A dynamic reputation incentive mechanism for federated learning in dense internet of vehicles

Federated Learning (FL) enables devices to use data locally for model training and thus has received significant attention for protecting data privacy in the Internet of Vehicles (IoV). However, rational vehicles are reluctant to contribute their data to participate in training without compensation, necessitating the implementation of effective incentive algorithms to motivate vehicles to participate in training. Nevertheless, unlike incentives in other domains, the IoV has the following challenges for the design of incentive systems. First, the large number of vehicles in a dense IoT imposes a huge communication burden and pressure on computational efficiency. Second, road data used by vehicles for training may be affected by factors such as damaged sensors or harsh environments, resulting in changes in data quality. Third, intermittent participation issues are caused by the vehicle’s mobility. To address these issues, we propose DriveFL: A Dynamic Reputation Incentive Mechanism for Federated Learning in Dense Internet of Vehicles. Specifically, we employ gradient compression techniques to reduce communication costs. Subsequently, we design a similarity-based gradient compression quality assessment method capable of evaluating the quality of vehicle data in real time. Then, we develop a dynamic reputation incentive mechanism that quantifies quality assessment records and integrates reverse auction theory, which can attract vehicles with higher data quality from those that intermittently participate in training, thereby enhancing model training quality under constrained communication costs and budget limitations. Theoretical analysis demonstrates that our mechanism satisfies computational efficiency, individual rationality, budget feasibility, and truthfulness. Simulation experiments confirm the effectiveness of our approach.