LATIDIA · Ciberseguridad

Aprendizaje federado personalizado con preservación de la privacidad (PPP-FL) para imágenes médicas

arXiv: 2610.27470v1Tipo de anuncio: nuevo Resumen: El aprendizaje federado (FL) es un paradigma de aprendizaje automático distribuido que reduce los costos de transmisión, mitiga las restricciones de intercambio de datos transfronterizos y mejora la privacidad b

La noticia

arXiv:2610.27470v1 Announce Type: new Abstract: Federated learning (FL) is a distributed machine learning paradigm that reduces transmission costs, mitigates cross-border data sharing restrictions, and enhances privacy by keeping raw data on local devices. Clients in FL systems train local models and share only updates, which are aggregated by a central server. While this setup limits direct data exposure, FL remains vulnerable to privacy attacks. Existing attack and defense strategies have largely been developed for centralized systems, and adapting them to FL introduces new challenges due to its unique architecture and training workflow. In this work, we first propose a model inversion attack on FL systems using Deep Convolutional Generative Adversarial