28 citations · 48 across the 23 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
Binary Linear Tree Commitment-based Ownership Protection for Distributed Machine Learning
Tianxiu Xie, Keke Gai, Jing Yu +1
Distributed machine learning enables parallel training of extensive datasets by delegating computing tasks across multiple workers. Despite the cost reduction benefits of distribut…
EASTER: Embedding Aggregation-based Heterogeneous Models Training in Vertical Federated Learning
Shuo Wang, Keke Gai, Jing Yu +3
Vertical federated learning has garnered significant attention as it allows clients to train machine learning models collaboratively without sharing local data, which protects the…