Publications (23)
Defending Against Backdoor Attacks via Alignment Checking in Model-Contrastive Federated Learning
Hongliang Zhang, Zhongyuan Yu, Guijuan Wang +4
The paper proposes FedDAB, a two‑phase defense for federated learning that uses contrastive regularization and alignment checking to detect and exclude malicious local updates caus…
Differentially Private Distance Query with Asymmetric Noise
Weihong Sheng, Jiajun Chen, Chunqiang Hu +3
With the growth of online social services, social information graphs are becoming increasingly complex. Privacy issues related to analyzing or publishing on social graphs are also…
ADP-VRSGP: Decentralized Learning with Adaptive Differential Privacy via Variance-Reduced Stochastic Gradient Push
Xiaoming Wu, Teng Liu, Xin Wang +2
Differential privacy is widely employed in decentralized learning to safeguard sensitive data by introducing noise into model updates. However, existing approaches that use fixed-v…
Edge Dominating Capability based Backbone Construction in Wireless Networks
Congcong Chen, Jiguo Yu, Xiujuan Zhang
Constructing a connected dominating set as the virtual backbone plays an important role in wireless networks. In this paper, we propose two novel approximate algorithms for dominat…
DFPL: Decentralized Federated Prototype Learning Across Heterogeneous Data Distributions
Hongliang Zhang, Fenghua Xu, Zhongyuan Yu +3
Federated learning is a distributed machine learning paradigm through centralized model aggregation. However, standard federated learning relies on a centralized server, making it…
FL-OA: A Byzantine-Robust Federated Learning Framework with Outsourced Auditing for Intelligent Devices
Hongliang Zhang, Zhongyuan Yu, Fenghua Xu +3
Federated learning (FL) enables multiple intelligent devices to collaboratively train a high-accuracy model without sharing raw data. However, due to its distributed nature, FL is…