  Federated Learning (FL) faces several challenges, such as the privacy of the
clients data and security against Byzantine clients. Existing works treating
privacy and security jointly make sacrifices on the privacy guarantee. In this
work, we introduce LoByITFL, the first communication-efficient
Information-Theoretic (IT) private and secure FL scheme that makes no
sacrifices on the privacy guarantees while ensuring security against Byzantine
adversaries. The key ingredients are a small and representative dataset
available to the federator, a careful transformation of the FLTrust algorithm
and the use of a trusted third party only in a one-time preprocessing phase
before the start of the learning algorithm. We provide theoretical guarantees
on privacy and Byzantine-resilience, and provide convergence guarantee and
experimental results validating our theoretical findings.
