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

Joint Task Offloading and Resource Orchestration in 6G SAGINs: A Hybrid Quantum-Inspired Meta-Heuristic Approach

Achieving global ubiquitous connectivity in 6G wireless systems necessitates the seamless integration of terrestrial networks with Space-Air-Ground Integrated Networks (SAGINs). However, joint task offloading and resource orchestration in such heterogeneous architectures involve large-scale combinatorial optimization challenges. This paper proposes a Hybrid Quantum-Inspired Meta-Heuristic (QIMH) approach to minimize system costs, effectively balancing latency and energy consumption. By utilizing quantum superposition principles and adaptive quantum rotation gates, the proposed algorithm enhances explorationexploitation capabilities. Furthermore, the optimization framework is implemented using vectorized tensor operations on an NVIDIA RTX 4090 GPU. Experimental results demonstrate that the proposed Hybrid QGA outperforms Particle Swarm Optimization (PSO) with up to 10.96% cost reduction. Notably, the algorithm maintains a near-constant execution time of approximately 0.25 seconds for up to 1000 users, proving its real-time feasibility for large-scale 6G non-terrestrial network applications.

Federated Learning of Satellite Aided Computation in LEO Ubiquitous Edge Computing Networks

With the rapid development of artificial intelligence and low-earth orbit (LEO) satellite edge computing technology, there has been a rapid increase in the demand for intelligence networks and services among users in remote areas, such as federated learning (FL). We propose a satellite aided computation FL (SACFL) system in LEO ubiquitous edge computing (UEC) networks, aiming at improving the efficiency of FL tasks in remote areas. In the considered deployment scenario, the following key factors are considered: 1) terrestrial users in remote areas; 2) offloading data to satellites for aided computation; and 3) global aggregation on satellite. However, the highly dynamic characteristics and the uneven distribution of satellite computation resources pose significant challenges to the low-delay requirements of satellite-based FL tasks. To this end, we formulate an optimization problem to minimize delay by jointly considering access selection, computation offloading, aggregation satellite (AgS) selection, and computation resource allocation. To solve the formulated problem, we propose a novel multi-agent alternating (M2A) optimization method. Specifically, three independent agents are trained alternately to make decisions on access selection, computation offloading, and AgS selection. Comprehensive simulations demonstrate that the proposed method outperforms other benchmark algorithms in terms of convergence, delay, and FL accuracy.

Quantum Federated Reinforcement Learning‐Based Traffic Offloading and Resource Allocation for RSMA‐Enabled Space–Air–Ground Integrated Networks

The evolution of sixth‐generation (6G) wireless networks demands ultra‐reliable low‐latency communication (URLLC), massive connectivity, and high‐capacity data transmission in highly dynamic environments. Space–Air–Ground Integrated Networks (SAGINs) have emerged as a promising architecture by seamlessly integrating satellites, unmanned aerial vehicles (UAVs), and terrestrial infrastructure to provide ubiquitous connectivity. However, stochastic traffic arrivals, UAV mobility, time‐varying wireless channels, and the coexistence of enhanced Mobile Broadband (eMBB) and URLLC services make traffic offloading and resource allocation highly challenging. These factors transform the optimization task into a stochastic mixed‐integer nonlinear programming (MINLP) problem. To address this challenge, this paper proposes a Quantum Federated Reinforcement Learning (QFRL)‐based traffic offloading framework for RSMA‐enabled SAGINs. The optimization problem is formulated as a constrained Markov decision process (CMDP), allowing distributed small cells to jointly optimize traffic offloading ratios, bandwidth allocation, RSMA power distribution, and UAV trajectory planning while satisfying stringent delay and reliability requirements. A variational quantum circuit (VQC)‐based actor‐critic architecture is developed to improve learning efficiency and policy representation in high‐dimensional continuous action spaces. In addition, a federated aggregation mechanism enables privacy‐preserving distributed learning and scalable coordination across the space, air, and ground segments. The proposed framework employs temporal‐difference learning and parameter‐shift gradient optimization to ensure stable convergence under stochastic network dynamics. Simulation results demonstrated that the proposed QFRL framework reduces traffic dropping probability by 28%–35%, decreases URLLC delay by 22%–30%, improves network availability by 18%–25%, and enhances traffic offloading efficiency by 20%–27% compared with Differentiated Federated Soft Actor‐Critic (DFSAC), Double Q‐Learning delay sensitive replay memory (DSRPM), and Nash Equilibrium Iteration Offloading (NEIO‐G) schemes, respectively.

Energy-Optimized Lightweight DRL for Computational Offloading in UAV-IoV Systems

The integration of 5G/6G networks with the Internet of Vehicles (IoV) requires efficient computational offloading for data-intensive applications such as autonomous driving and augmented reality. Although Unmanned Aerial Vehicles (UAVs) offer agile mobile edge computing (MEC) capabilities, their operational efficiency is hampered by high mobility, limited battery life, and the complexity of joint resource optimization. Existing offloading strategies often fail to simultaneously optimize latency, energy consumption, and resource utilization under dynamic IoV conditions. This paper proposes a novel Energy-Optimized Lightweight Deep Reinforcement Learning (DRL) framework for intelligent task offloading in UAV-assisted IoV networks. Our approach leverages a simplified Double Deep Q-Network (DDQN) to dynamically manage task partitioning by intelligent offloading decisions, UAV trajectory planning through optimized path forecasting, and resource allocation through adaptive computation distribution. Key innovations include a streamlined state-space design that reduces computational overhead by 30% and a composite reward function that balances latency and energy objectives. These are realized by a prioritized experience replay mechanism and a target network separation strategy that enhances learning stability. Experimental results demonstrate that our framework achieves a task success rate of 98.5%, reduces latency by 40%, and maintains a 78.1%. The results confirm the framework’s superiority, demonstrating significant improvements over its base architecture (DQN), its enhanced variant (DDQN), and other state-of-the-art baselines like MADDPG and game-theoretic approaches, thereby providing a robust solution for practical UAV-IoV deployments.

Communication-Aware Federated Learning for Energy Management in Edge-Cloud Autonomous Systems

Dual-Layer Over-the-Air Federated Learning in LEO Satellite Networks: Architecture, Key Technologies and Applications

This article outlines the fundamental principles of the dual-layer OTA model and introduces the adaptive BH mechanism designed for time-varying topologies, aiming to provide insights for the evolution of ubiquitous non-terrestrial intelligence.