Fed-TaLoRA uses task-agnostic low-rank residual adaptation with post-aggregation calibration to enable efficient federated continual fine-tuning across sequential tasks under non-IID conditions.
Communication-efficient learning of deep networks from decentralized data,
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FedMAGS applies GAT-Seq2Seq modeling inside a federated meta-RL loop to optimize offloading decisions for complex DAG tasks across distributed vehicular edge servers.
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Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning
Fed-TaLoRA uses task-agnostic low-rank residual adaptation with post-aggregation calibration to enable efficient federated continual fine-tuning across sequential tasks under non-IID conditions.
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Heterogeneous Tasks Offloading in Vehicular Edge Computing: A Federated Meta Deep Reinforcement Learning Approach
FedMAGS applies GAT-Seq2Seq modeling inside a federated meta-RL loop to optimize offloading decisions for complex DAG tasks across distributed vehicular edge servers.