A Learning-Based Contract Mechanism for Privacy-Aware Incentives in UAV-Assisted Federated Learning 

A Learning-Based Contract Mechanism for Privacy-Aware Incentives in UAV-Assisted Federated Learning is a web-based simulation and decision-support platform designed to manage reliable participant involvement, privacy control, and incentive distribution in UAV-supported federated learning environments. The system enables users to register, log in, submit learning-related data, choose privacy-aware contract options, apply noise protection, view incentives, and receive reward information. UAV nodes can create tasks, view submitted data, collect local updates, aggregate model information, evaluate metrics, and manage the federated model lifecycle. The admin module supports simulation control, participant monitoring, social welfare evaluation, individual rationality and incentive compatibility checking, charts, and report generation. The proposed mechanism applies learning-based contract selection to balance model contribution, privacy cost, noise level, participation willingness, and reward allocation. By integrating privacy-aware noise control with contract-based incentive modeling, the system encourages truthful participation while reducing privacy risk. The project demonstrates how UAV-assisted federated learning can support distributed intelligence without directly sharing raw user data. It is suitable for applications such as smart monitoring, disaster response, communication networks, and mobile edge intelligence where UAVs collect or coordinate learning updates from distributed users.

Keywords: UAV-Assisted Federated Learning; Contract Theory; Privacy-Aware Incentives; Noise Injection; Reward Allocation; Learning-Based Mechanism; Individual Rationality; Incentive Compatibility; Social Welfare; Distributed Intelligence