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.
Prioritized Information Bottleneck Theoretic Framework With Distributed Online Learning for Edge Video Analytics,
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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.
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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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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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.