Federated learning (FL) has emerged as a powerful machine learning technique that
enables the development of models from decentralized data sources. However, the …

Vertical federated learning (VFL) is being used more and more widely in industry. One of its
most common application scenarios is a two-party setting: a participant (ie, the host), who …

Vertical Federated Learning (VFL) facilitates collaboration on model training among multiple
parties, each owning partitioned features of the distributed dataset. Although backdoor …

In federated learning systems, the participants collaboratively train a joint model without
sharing their raw data. However, these systems are susceptible to poisoning attacks, due to …

Vertical Federated Learning (VFL) is a privacy-preserving distributed learning paradigm
where different parties collaboratively learn models using partitioned features of shared …

Federated Learning has emerged as a revolutionary technology in Machine Learning (ML),
enabling collaborative training of models in a distributed environment while ensuring privacy …

纵向联邦学习(vertical federated learning, VFL) 常用于高风险场景中的跨领域数据共享,
用户需要理解并信任模型决策以推动模型应用. 现有研究主要关注VFL 中可解释性与隐私之间的 …

Federated Learning (FL) has garnered widespread adoption across various domains such
as finance, healthcare, and cybersecurity. Nonetheless, FL remains under significant threat …

In recent years, the neural network backdoor hidden in the parameters of the federated
learning model has been proved to have great security risks. Considering the characteristics …

요 약연합학습 (Federated Learning) 에서는 여러 참가자가 서로 간의 데이터를 공유하지 않고
협력하여하나의 모델을 학습할 수 있다. 그 중 수직 연합학습 (Vertical Federated Learning) 은 …