Energy-Aware Federated Distillation via Quantum-Driven Task Offloading in LEO Satellite Networks

Abstract

Low earth orbit (LEO) satellite networks have emerged as a key enabler for delivering real-time and global services to distributed terrestrial nodes, particularly in remote regions. To preserve data privacy, federated learning (FL) provides a decentralized framework for advancing artificial intelligence (AI) in complex tasks. However, the efficiency of FL is constrained by high and imbalanced energy consumption, which limits its practical deployment. To address these challenges, an energy-aware FL framework that integrates knowledge distillation (KD) with task offloading is proposed, where KD is performed at both the FL server and client devices or direct-connected satellites using public datasets. The energy consumption balancing problem is formulated as a quadratic unconstrained binary optimization (QUBO) model. To achieve computational efficiency and parallelism, the quantum approximate optimization algorithm (QAOA) is employed to solve the problem with both the mixing and cost Hamiltonians derived and the corresponding quantum circuit designed. In a FL framework over a LEO satellite network comprising 40 satellites and 10 FL clients, the proposed method reduces energy consumption by approximately 26.4%, achieves improved energy balance with a weighted variance of approximately 4.93 and maintains high accuracy of 0.95 in a vehicle classification task, compared with the traditional FL method.