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Found 8 papers, 1 papers with code
Split Federated Learning Over Heterogeneous Edge Devices: Algorithm and Optimization
Split Learning (SL) is a promising collaborative machine learning approach, enabling resource-constrained devices to train models without sharing raw data, while reducing computational load and preserving privacy simultaneously.
Faster Convergence on Heterogeneous Federated Edge Learning: An Adaptive Clustered Data Sharing Approach
Federated Edge Learning (FEEL) emerges as a pioneering distributed machine learning paradigm for the 6G Hyper-Connectivity, harnessing data from the Internet of Things (IoT) devices while upholding data privacy.
The SkatingVerse Workshop & Challenge: Methods and Results
The SkatingVerse Workshop & Challenge aims to encourage research in developing novel and accurate methods for human action understanding.
M-Net: A Lightweight Network Based on Multilayer Perceptron for Massive MIMO CSI Feedback
Finally, we demonstrate that the M-Net variants achieve the SOTA performance by only deep enning the M-Net decoder.
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.
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.
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.
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.
