"Security in Federated Learning and Fed-BioMed" by Riccardo Taiello and Lucia Innocenti

  • Science outreach
Published on December 5, 2023 Updated on December 13, 2023

Discover the third and last article of the Federated Learning series on Medium.

In the context of Federated Learning (FL), it has come to light that even the sharing of local model parameters can reveal sensitive information about the clients’ training data. Recent studies [1] have unveiled various attack methods, such as membership inference or model inversion.

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