6 papers
Robust Dynamic Expansion for Continual Learning under Backdoor Attacks via Purification and Selective Recovery
Keyu Lin, Fei Ye, Qihe Liu +2
Continual learning (CL) enables models to acquire new knowledge from sequentially arriving tasks while retaining previously learned knowledge. However, in practical scenarios, task…
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…
Defending Against Backdoor Attacks via Alignment Checking in Model-Contrastive Federated Learning
Hongliang Zhang, Zhongyuan Yu, Guijuan Wang +4
Federated Learning (FL) is vulnerable to backdoor attacks because of its distributed nature in edge computing scenarios. Existing defense methods show limited efficacy as they over…
Towards Performance-Enhanced Model-Contrastive Federated Learning using Historical Information in Heterogeneous Scenarios
Hongliang Zhang, Jiguo Yu, Guijuan Wang +4
Federated Learning (FL) enables multiple nodes to collaboratively train a model without sharing raw data. However, FL systems are usually deployed in heterogeneous scenarios, where…
Local Distance Query with Differential Privacy
Weihong Sheng, Jiajun Chen, Bin Cai +3
Differential Privacy (DP) is commonly employed to safeguard graph analysis or publishing. Distance, a critical factor in graph analysis, is typically handled using curator DP, wher…
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…