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cs.LG2026
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…
cs.LG2026
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…
cs.LG2026
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…