Poster Abstract: Compromising Federated Medical AI-Backdoor Risks in Prompt Learning

This paper investigates the security vulnerabilities of prompt-learning-based FL systems in a healthcare setting. Specifically, we use a backdoor attack that leverages learnable prompt vectors in vision-language medical foundation models to execute stealthy adversarial manipulations. We evaluate our attack across diverse healthcare datasets and FL configurations, showing that while FL is useful as a privacy-preserving mechanism, it is susceptible to targeted backdoor attacks that pose a threat to medical applications.