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Federated learning is a collaborative method that aims to preserve dataprivacy while creating AI models. Current approaches to federated learning tendto rely heavily on secure aggregation protocols to preserve data privacy.However, to some degree, such protocols assume that the entity orchestratingthe federated learning process (i.e., the server) is not fully malicious ordishonest. We investigate vulnerabilities to secure aggregation that couldarise if the server is fully malicious and attempts to obtain access toprivate, potentially sensitive data. Furthermore, we provide a method tofurther defend against such a malicious server, and demonstrate effectivenessagainst known attacks that reconstruct data in a federated learning setting.

Secure Aggregation Is Not All You Need: Mitigating Privacy Attacks with Noise Tolerance in Federated Learning