9 citations · 23 across the 8 of their papers we have counts for
6 papers · 1 filter
Feature Matching Data Synthesis for Non-IID Federated Learning
Zijian Li, Yuchang Sun, Jiawei Shao +3
Federated learning (FL) has emerged as a privacy-preserving paradigm that trains neural networks on edge devices without collecting data at a central server. However, FL encounters…
pFedSim: Similarity-Aware Model Aggregation Towards Personalized Federated Learning
Jiahao Tan, Yipeng Zhou, Gang Liu +2
The federated learning (FL) paradigm emerges to preserve data privacy during model training by only exposing clients' model parameters rather than original data. One of the biggest…
Federated Learning with GAN-based Data Synthesis for Non-IID Clients
Zijian Li, Jiawei Shao, Yuyi Mao +2
Federated learning (FL) has recently emerged as a popular privacy-preserving collaborative learning paradigm. However, it suffers from the non-independent and identically distribut…
Decentralized Stochastic Proximal Gradient Descent with Variance Reduction over Time-varying Networks
Xuanjie Li, Yuedong Xu, Jessie Hui Wang +2
In decentralized learning, a network of nodes cooperate to minimize an overall objective function that is usually the finite-sum of their local objectives, and incorporates a non-s…
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…
Faster Activity and Data Detection in Massive Random Access: A Multi-armed Bandit Approach
Jialin Dong, Jun Zhang, Yuanming Shi +1
This paper investigates the grant-free random access with massive IoT devices. By embedding the data symbols in the signature sequences, joint device activity detection and data de…