74 citations · 113 across the 17 of their papers we have counts for
7 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…
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…
FedFA: Federated Learning with Feature Anchors to Align Features and Classifiers for Heterogeneous Data
Tailin Zhou, Jun Zhang, Danny H. K. Tsang
Federated learning allows multiple clients to collaboratively train a model without exchanging their data, thus preserving data privacy. Unfortunately, it suffers significant perfo…
DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharing
Jiawei Shao, Yuchang Sun, Songze Li +1
Federated learning (FL) strives to enable collaborative training of machine learning models without centrally collecting clients' private data. Different from centralized training,…