  In Federated Learning (FL), common privacy-enhancing techniques, 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 an
adversarial server. Lotto supports two selection algorithms: random and
informed. To ensure random selection without a trusted server, Lotto enables
each client to autonomously determine their participation using verifiable
randomness. For informed selection, which is more vulnerable to manipulation,
Lotto approximates the algorithm by employing random selection within a refined
client pool. Our theoretical analysis shows that Lotto effectively aligns the
proportion of server-selected compromised participants with the base rate of
dishonest clients in the population. Large-scale experiments further reveal
that Lotto achieves time-to-accuracy performance comparable to that of insecure
selection methods, indicating a low computational overhead for secure
selection.
