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.
AoI-Aware Resource Management for Smart Health via Deep Reinforcement Learning
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
A new dual-timescale FCL framework with layer-selective rehearsal and knowledge recovery improves mIoU by up to 8.3% in federated settings for autonomous systems.
citing papers explorer
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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.
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Lifecycle-Aware Federated Continual Learning in Mobile Autonomous Systems
A new dual-timescale FCL framework with layer-selective rehearsal and knowledge recovery improves mIoU by up to 8.3% in federated settings for autonomous systems.