Core Concepts
Lotto provides secure participant selection in Federated Learning to prevent manipulation by adversarial servers.
Abstract
Lotto addresses the issue of secure participant selection in Federated Learning to prevent adversarial servers from manipulating client selection. It introduces random and informed selection algorithms, ensuring fairness and security. By incorporating verifiable randomness and population refinement, Lotto aligns the proportion of compromised participants with the base rate of dishonest clients. The protocol guarantees consistency and security throughout the training process, maintaining privacy while maximizing utility.
Stats
SecAgg safeguards a participant i’s plaintext update xxxi from server probing by distributing secret shares of sk1i and bi among other participants.
Distributed DP ensures that no specific client’s participation significantly increases the likelihood of any observed aggregated update by potential adversaries.