PRISM maintains per-expert gradient subspace bases preserved under FedAvg to resolve spurious isolation in federated multimodal continual learning, outperforming 16 baselines with larger gains on longer task sequences.
A Stochastic Geometry- Based Analysis of SWIPT-Assisted Underlaid Device-to-Device Energy Harvesting,
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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.
SCALE introduces a sensitivity-aware federated unlearning method with adaptive sparsification and freshness optimization to achieve better forgetting performance in MEC systems than prior baselines.
citing papers explorer
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PRISM: Exposing and Resolving Spurious Isolation in Federated Multimodal Continual Learning
PRISM maintains per-expert gradient subspace bases preserved under FedAvg to resolve spurious isolation in federated multimodal continual learning, outperforming 16 baselines with larger gains on longer task sequences.
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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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SCALE: Sensitivity-Aware Federated Unlearning with Information Freshness Optimization for Mobile Edge Computing
SCALE introduces a sensitivity-aware federated unlearning method with adaptive sparsification and freshness optimization to achieve better forgetting performance in MEC systems than prior baselines.