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Preserving Privacy and Security in Federated Learning

2022 · arXiv (Cornell University)

Federated learning is known to be vulnerable to both security and privacy issues. Existing research has focused either on preventing poisoning attacks from users or on concealing the local model updates from the server, but …

Active Membership Inference Attack under Local Differential Privacy in Federated Learning

2023 · arXiv (Cornell University)

Federated learning (FL) was originally regarded as a framework for collaborative learning among clients with data privacy protection through a coordinating server. In this paper, we propose a new active membership inference (AMI) attack carried …

Privacy Analysis of Federated Learning via Dishonest Servers

2023

Federated Learning (FL) has gained popularity for its ability to improve model training while protecting user privacy. However, recent studies have shown that FL can be vulnerable to active reconstruction attacks by dishonest servers. Specifically, …