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 Review of Continual Learning in Edge AI,
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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.
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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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.
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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.