Adaptive offloading in multi-access edge networks via hierarchical federated learning and real-time system adaptation

Achieving ultra-reliable real-time digital twin (DT) adaptation in mobile edge environments requires intelligent orchestration of computation and communication under user heterogeneity and dynamic mobility. This paper introduces GADENet, a graph attention-enhanced digital twin evolution network that fuses graph neural modelling, multi-agent actor-critic learning, and hierarchical federated personalisation to enable seamless digital representations of user equipment (UE) in distributed edge networks. At its core, GADENet employs a GAT-assisted multi-agent deep deterministic policy gradient (MADDPG) framework to jointly learn optimal DT migration and personalisation strategies across edge servers, guided by real-time traffic topologies and resource interdependencies. Each DT model is modularised into generalisable and adaptive subspaces, trained collaboratively through a three-tier edge-cloud federated loop and refined using localised attention-based updates. For efficient mobility handling, we propose a parameter-sliced DT relay protocol that selectively migrates the minimal personalisation subset across servers, leveraging learned action-value functions to minimise response latency. Extensive simulations on CIFAR-based datasets and synthetic edge workloads demonstrate that GADENet achieves up to 30% reduction in interaction latency and significantly boosts modelling fidelity versus strong federated and DRL-based baselines. This work offers a principled blueprint for intelligent DT deployment under the constraints of 6G and next-gen IoT fabrics.