介绍 知识蒸馏是一种用于监督“学生”神经网络训练的通用技术，它通过捕获和转移训练过的“教师”网络的知识来实现。 虽然最初的动机是为了资源高效深度学习的神经网络压缩任务，但知识蒸馏已经在特权学习、对抗性防御 和噪声数据学习 等领域找到了更广泛的应用。 知识蒸馏在概念上很简单:它通过额外的蒸馏损失来指导学生网络的训练，从而鼓励学生模仿教. Nov 01, 2022 · It employs a two-sided knowledge distillation with contrastive learning as a core component, allowing the federated system to function without requiring clients to share any data features.. Oct 25, 2020 · Federated learning is a new scheme of distributed machine learning, which enables a large number of edge computing devices to jointly learn a shared model without private data sharing. Federated learning allows nodes to synchronize only the locally trained models instead of their own private data, which provides a guarantee for privacy and security. However, due to the challenges of ....
In Federated Learning (FL), data communication among clients is denied. However, it is difficult to learn from the decentralized client data, which is under-sampled, especially for segmentation.
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Federated Unlearning with Knowledge Distillation. Federated Learning (FL) is designed to protect the data privacy of each client during the training process by transmitting only models instead of the original data. However, the trained model may memorize certain information about the training data. With the recent legislation on right to be.
Federated Learning (FL) has gained unprecedented growth in the past few years by facilitating data privacy. This poster proposes a network resource aware federated learning approach that utilizes the concept of knowledge distillation to train a machine learning model by using local data samples. The approach creates different groups based on the bandwidth between clients and server and.
软件学报英文版. Abstract:With the emergence and accumulation of massive data,data governance has become an important manner to improve data quality and.
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