26 citations · 52 across the 26 of their papers we have counts for
5 papers · 1 filter
FedCiR: Client-Invariant Representation Learning for Federated Non-IID Features
Zijian Li, Zehong Lin, Jiawei Shao +2
Federated learning (FL) is a distributed learning paradigm that maximizes the potential of data-driven models for edge devices without sharing their raw data. However, devices ofte…
A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency
Jiawei Shao, Zijian Li, Wenqiang Sun +6
Federated learning (FL) has emerged as a secure paradigm for collaborative training among clients. Without data centralization, FL allows clients to share local information in a pr…
Large Language Models Empowered Autonomous Edge AI for Connected Intelligence
Yifei Shen, Jiawei Shao, Xinjie Zhang +5
The evolution of wireless networks gravitates towards connected intelligence, a concept that envisions seamless interconnectivity among humans, objects, and intelligence in a hyper…
Channel and Gradient-Importance Aware Device Scheduling for Over-the-Air Federated Learning
Yuchang Sun, Zehong lin, Yuyi Mao +2
Federated learning (FL) is a popular privacy-preserving distributed training scheme, where multiple devices collaborate to train machine learning models by uploading local model up…
Understanding and Improving Model Averaging in Federated Learning on Heterogeneous Data
Tailin Zhou, Zehong Lin, Jun Zhang +1
Model averaging is a widely adopted technique in federated learning (FL) that aggregates multiple client models to obtain a global model. Remarkably, model averaging in FL yields a…