Ensemble Distillation For Robust Model Fusion In Federated Learning. Training the central classifier through unlabeled data on the outputs of the models from the clients. Ensemble distillation for robust model fusion in federated learning.
Ensemble distillation for robust model fusion in federated learning论文笔记 from blog.csdn.net
Ensemble distillation for robust model fusion in federated learning. Specifically, we propose ensemble distillation for model fusion, i.e. Federated learning (fl) is a machine learning setting where many devices collaboratively train a machine learning model while keeping the training data decentralized.
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This work proposes ensemble distillation for model fusion, i.e. Specifically, we propose ensemble distillation for model fusion, i.e. Training the central classifier through unlabeled data on the outputs of the models from the clients.
Specifically, We Propose Ensemble Distillation For Model Fusion, I.e.
Federated learning (fl) is a machine learning setting where many devices collaboratively train a machine. Training the central classifier through unlabeled data on the outputs of the models from the clients, which allows. Ensemble distillation for robust model fusion in federated learning.
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Training the central classifier through unlabeled data on the outputs of the models from the clients. This paper proposed a model fusion federated learning method fedmd, which use ensemble distillation for robust model fusion. Federated learning (fl) is a machine learning setting where many devices collaboratively train a machine learning model while keeping the training data decentralized.
