Trust Management of Tiny Federated Learning in Internet of Unmanned Aerial Vehicles

Lightweight training and distributed tiny data storage in local model will lead to the severe challenge of convergence for tiny federated learning (FL). Achieving fast convergence in tiny FL is crucial for many emerging applications in Internet of Unmanned Aerial Vehicles (IUAVs) networks. Excessive information exchange between UAVs and IoT devices could lead to security risks and data breaches, while insufficient information can slow down the learning process and negatively system performance experience due to significant computational and communication constraints in tiny FL hardware system. This paper proposes a trusting, low latency, and energy-efficient tiny wireless FL framework with blockchain (TBWFL) for IUAV systems. We develop a quantifiable model to determine the trustworthiness of IoT devices in IUAV networks. This model incorporates the time spent in communication, computation, and block production with a decay function in each round of FL at the UAVs. Then it combines the trust information from different UAVs, considering their credibility of trust recommendation. We formulate the TBWFL as an optimization problem that balances trustworthiness, learning speed, and energy consumption for IoT devices with diverse computing and energy capabilities. We decompose the complex optimization problem into three sub-problems for improved local accuracy, fast learning, trust verification, and energy efficiency of IoT devices. Our extensive experiments show that TBWFL offers higher trustworthiness, faster convergence, and lower energy consumption than the existing state-of-the-art FL scheme.