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cs.LG2025
A Vision-Language Pre-training Model-Guided Approach for Mitigating Backdoor Attacks in Federated Learning
Keke Gai, Dongjue Wang, Jing Yu +2
Defending backdoor attacks in Federated Learning (FL) under heterogeneous client data distributions encounters limitations balancing effectiveness and privacy-preserving, while mos…
cs.LG2025
Vertical Federated Continual Learning via Evolving Prototype Knowledge
Shuo Wang, Keke Gai, Jing Yu +2
Vertical Federated Learning (VFL) has garnered significant attention as a privacy-preserving machine learning framework for sample-aligned feature federation. However, traditional…
cs.LG2025
Adaptive Prototype Knowledge Transfer for Federated Learning with Mixed Modalities and Heterogeneous Tasks
Keke Gai, Mohan Wang, Jing Yu +2
Multimodal Federated Learning (MFL) with mixed modalities enables unimodal and multimodal clients to collaboratively train models while ensuring clients' privacy. As a representati…