Lotto: Secure Participant Selection against Adversarial Servers in Federated Learning. (arXiv:2401.02880v1 [cs.CR])

In Federated Learning (FL), common privacy-preserving technologies, such as
secure aggregation and distributed differential privacy, rely on the critical
assumption of an honest majority among participants to withstand various
attacks. In practice, however, servers are not always trusted, and an
adversarial server can strategically select compromised clients to create a
dishonest majority, thereby undermining the system's security guarantees. In
this paper, we present Lotto, an FL system that addresses this fundamental, yet
underexplored issue by providing secure participant selection against …

adversarial aggregation attacks clients compromised critical differential privacy distributed federated federated learning practice privacy select server servers technologies