TiLP integrates network, training, and task sub-twins into a digital twin and uses receding-horizon cross-entropy planning with actor-critic guidance to jointly optimize resource allocation in federated split learning, improving task success by 9.5 percentage points on robotic tasks.
DNN Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approach,
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Application-Aware Twin-in-the-Loop Planning for Federated Split Learning over Wireless Edge Networks
TiLP integrates network, training, and task sub-twins into a digital twin and uses receding-horizon cross-entropy planning with actor-critic guidance to jointly optimize resource allocation in federated split learning, improving task success by 9.5 percentage points on robotic tasks.