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.
FedTD3: An Accelerated Learning Approach for UA V Trajectory Planning
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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.
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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.