74 citations · 113 across the 17 of their papers we have counts for
11 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…
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…
Binary Federated Learning with Client-Level Differential Privacy
Lumin Liu, Jun Zhang, Shenghui Song +1
Federated learning (FL) is a privacy-preserving collaborative learning framework, and differential privacy can be applied to further enhance its privacy protection. Existing FL sys…
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…
DABS: Data-Agnostic Backdoor attack at the Server in Federated Learning
Wenqiang Sun, Sen Li, Yuchang Sun +1
Federated learning (FL) attempts to train a global model by aggregating local models from distributed devices under the coordination of a central server. However, the existence of…
Task-Oriented Communication with Out-of-Distribution Detection: An Information Bottleneck Framework
Hongru Li, Wentao Yu, Hengtao He +4
Task-oriented communication is an emerging paradigm for next-generation communication networks, which extracts and transmits task-relevant information, instead of raw data, for dow…
Low-complexity Deep Video Compression with A Distributed Coding Architecture
Xinjie Zhang, Jiawei Shao, Jun Zhang
Prevalent predictive coding-based video compression methods rely on a heavy encoder to reduce temporal redundancy, which makes it challenging to deploy them on resource-constrained…
Selective Knowledge Sharing for Privacy-Preserving Federated Distillation without A Good Teacher
Jiawei Shao, Fangzhao Wu, Jun Zhang
While federated learning is promising for privacy-preserving collaborative learning without revealing local data, it remains vulnerable to white-box attacks and struggles to adapt…
AC2C: Adaptively Controlled Two-Hop Communication for Multi-Agent Reinforcement Learning
Xuefeng Wang, Xinran Li, Jiawei Shao +1
Learning communication strategies in cooperative multi-agent reinforcement learning (MARL) has recently attracted intensive attention. Early studies typically assumed a fully-conne…
LDMIC: Learning-based Distributed Multi-view Image Coding
Xinjie Zhang, Jiawei Shao, Jun Zhang
Multi-view image compression plays a critical role in 3D-related applications. Existing methods adopt a predictive coding architecture, which requires joint encoding to compress th…