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Clustered Data Sharing for Non-IID Federated Learning over Wireless Networks
Federated Learning (FL) is a novel distributed machine learning approach to leverage data from Internet of Things (IoT) devices while maintaining data privacy.
Multi scale Feature Extraction and Fusion for Online Knowledge Distillation
Moreover, we aggregate and fuse the former processed feature maps via feature fusion to assist the training of student models.
Asymptotic Soft Cluster Pruning for Deep Neural Networks
Filter pruning method introduces structural sparsity by removing selected filters and is thus particularly effective for reducing complexity.
An Adaptive Device-Edge Co-Inference Framework Based on Soft Actor-Critic
Recently, the applications of deep neural network (DNN) have been very prominent in many fields such as computer vision (CV) and natural language processing (NLP) due to its superior feature extraction performance.
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