1.7k citations · 6.2k across the 106 of their papers we have counts for
5 papers · 2 filters
Semi-Decentralized Federated Edge Learning with Data and Device Heterogeneity
Yuchang Sun, Jiawei Shao, Yuyi Mao +2
Federated edge learning (FEEL) has attracted much attention as a privacy-preserving paradigm to effectively incorporate the distributed data at the network edge for training deep l…
Communication-Computation Efficient Device-Edge Co-Inference via AutoML
Xinjie Zhang, Jiawei Shao, Yuyi Mao +1
Device-edge co-inference, which partitions a deep neural network between a resource-constrained mobile device and an edge server, recently emerges as a promising paradigm to suppor…
Communication-Efficient Federated Learning with Dual-Side Low-Rank Compression
Zhefeng Qiao, Xianghao Yu, Jun Zhang +1
Federated learning (FL) is a promising and powerful approach for training deep learning models without sharing the raw data of clients. During the training process of FL, the centr…
Decentralized Statistical Inference with Unrolled Graph Neural Networks
He Wang, Yifei Shen, Ziyuan Wang +4
In this paper, we investigate the decentralized statistical inference problem, where a network of agents cooperatively recover a (structured) vector from private noisy samples with…
Hierarchical Federated Learning with Quantization: Convergence Analysis and System Design
Lumin Liu, Jun Zhang, Shenghui Song +1
Federated learning (FL) is a powerful distributed machine learning framework where a server aggregates models trained by different clients without accessing their private data. Hie…