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cs.LG2025
Mitigating Membership Inference Vulnerability in Personalized Federated Learning
Kangsoo Jung, Sayan Biswas, Catuscia Palamidessi
Federated Learning (FL) has emerged as a promising paradigm for collaborative model training without the need to share clients' personal data, thereby preserving privacy. However,…
cs.LG2025
Metric Privacy in Federated Learning for Medical Imaging: Improving Convergence and Preventing Client Inference Attacks
Judith Sáinz-Pardo Díaz, Andreas Athanasiou, Kangsoo Jung +2
Federated learning is a distributed learning technique that allows training a global model with the participation of different data owners without the need to share raw data. This…