Federated edge learning (FEEL) is a prospective paradigm enabling edge devices to collaboratively participate in machine learning model training, unlocking countless opportunities for edge intelligence. As an extension of FEEL, federated synergy learning (FSyL) alleviates the computation and communication burdens on re...
Shu-Cun Fu, Fang Dong, Xiao-Long Xu et al.· IEEE Transactions on Mobile...· 1 citation
It is argued that evaluation should move beyond static question answering and model accuracy towards workflow-level assessment of evidence quality, tool use, policy and invariant compliance, staged execution, recovery, calibration, cost, and human intervention.
Muhammad Bilal, Jon Crowcroft, Rui-Zhi Wang et al.· arXiv.org· 3 citations· ⚡1
Experimental results show that the proposed GuardMLLM improves performance on tasks such as predicting patient mortality and ICU length of stay, and effectively alleviates overconfidence in LLM.
Gang Fu, Xiao-Long Xu, Hao-Long Xiang et al.· IEEE journal of biomedical a...· 0 citations
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