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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…