Tag: protection
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On Model Protection in Federated Learning against Eavesdropping Attacks
On Model Protection in Federated Learning against Eavesdropping Attacks arXiv:2504.02114v1 Announce Type: cross Abstract: In this study, we investigate the protection offered by federated learning algorithms against eavesdropping adversaries. In our model, the adversary is capable of intercepting model updates transmitted from clients to the server, enabling it to create its own estimate of the…
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Algorithm Protection in the Context of Federated Learning
Algorithm Protection in the Context of Federated Learning While working at a biotech company, we aim to advance ML & AI Algorithms to enable, for example, brain lesion segmentation to be executed at the hospital/clinic location where patient data resides, so it is processed in a secure manner. This, in essence, is guaranteed by federated…