Author

Shucun Fu

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#federated learning Oct 2026

CODEA: Online Container Deployment and Edge Association for Multi-Edge Federated Synergy Learning

Federated edge learning (FEEL) is a prospective paradigm enabling edge devices to collaboratively participate in machine learning model training, unlocking countless opportunities for edge intelligence. As an extension of FEEL, federated synergy learning (FSyL) alleviates the computation and communication burdens on resource-constrained end-devices by offloading partial model layers to edge servers for synergistic training. However, existing work largely ignores the impact of budgeted multi-edge service deployment and dynamic device-server association on training performance, causing significant accuracy degradation and increased costs. To address these limitations, this paper investigates critical performance bottlenecks of executing FSyL and formulates a novel benefit maximization problem that jointly optimizes online container deployment and edge association. To efficiently tackle this intractable problem, we propose CODEA, an online COntainer Deployment and Edge Association framework based on the contextual multi-armed bandit model. CODEA guides deploying containerized FSyL services across multiple edge servers under a budget constraint, maximizing device coverage and training robustness before each global update stage. Following container deployment, the online edge association determines the device-server association during each local training round, maximizing cost-savings and ensuring the success rate of update collection. Extensive experiments demonstrate that CODEA significantly improves training accuracy and cost-savings compared to state-of-the-art methods.

Shu-Cun Fu, Fang Dong, Xiao-Long Xu et al. · IEEE Transactions on Mobile... · 1 citation

2026

An Efficient Docking-Point Deployment and Charging Access Coordination Method for Embodied-Enhanced UAV Networks

As embodied intelligent agents, uncrewed aerial vehicles (UAVs) support low-altitude urban services, but their endurance is fundamentally constrained by limited onboard battery capacity. Existing solutions in dense urban environments incur high deployment costs, use coarse spatial layouts, and do not scale to large UAV fleets. We instead retrofit existing urban deployable infrastructure (UDI), such as traffic lights, street lamps, and communication base stations, as UAV docking points with charging capability. This UDI-based approach raises two coupled challenges: city-scale docking-point deployment over massive, spatially heterogeneous candidates, and coordinated multi-UAV access under queueing delays and residual-energy safety constraints. We jointly model docking queues, load, and energy consumption, and formulate a multi-objective optimization balancing energy consumption and load. To address these NP-hard deployment and scheduling subproblems, we propose a hierarchical UDI-based docking-point deployment algorithm (HUDD) that generates a scalable docking layout, and a charging access coordination algorithm based on convex relaxation and iterative rounding (CRIR) that coordinates energy-feasible, congestion-aware access for multiple UAVs on the obtained layout. Simulations on realistic urban datasets show that HUDD-CRIR outperforms baseline schemes in terms of energy consumption, response delay, queueing delay, and load distribution.

Wei Yang, Jiajie Xu, Jie Chen et al. · IEEE Transactions on Cogniti... · 0 citations