2023
DOI: 10.1109/access.2023.3254303
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Intelligent Recommendation for Departments Based on Medical Knowledge Graph

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Cited by 4 publications
(1 citation statement)
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“…In FL, the most direct solution for client drift [66] is to learn from other models, including mutual learning [183] between local models or between local and global models. In [157], Wasserstein distance [116] and regularization terms are introduced into the objective function of federated knowledge distillation to reduce the distribution difference between the global model and client models. [75] proposes a new model aggregation architecture to aggregate models by evaluating the effectiveness of deep neural networks (DNN) and using KD and the uncertainty quantification method of DNN.…”
Section: Kd-based Fl Methods For Non-iid Challengementioning
confidence: 99%
“…In FL, the most direct solution for client drift [66] is to learn from other models, including mutual learning [183] between local models or between local and global models. In [157], Wasserstein distance [116] and regularization terms are introduced into the objective function of federated knowledge distillation to reduce the distribution difference between the global model and client models. [75] proposes a new model aggregation architecture to aggregate models by evaluating the effectiveness of deep neural networks (DNN) and using KD and the uncertainty quantification method of DNN.…”
Section: Kd-based Fl Methods For Non-iid Challengementioning
confidence: 99%