  Federated Learning Networks (FLNs) have been envisaged as a promising
paradigm to collaboratively train models among mobile devices without exposing
their local privacy data. Due to the need for frequent model updates and
communications, FLNs are vulnerable to various attacks (e.g., eavesdropping
attacks, inference attacks, poisoning attacks, and backdoor attacks). Balancing
privacy protection with efficient distributed model training is a key challenge
for FLNs. Existing countermeasures incur high computation costs and are only
designed for specific attacks on FLNs. In this paper, we bridge this gap by
proposing the Covert Communication-based Federated Learning (CCFL) approach.
Based on the emerging communication security technique of covert communication
which hides the existence of wireless communication activities, CCFL can
degrade attackers' capability of extracting useful information from the FLN
training protocol, which is a fundamental step for most existing attacks, and
thereby holistically enhances the privacy of FLNs. We experimentally evaluate
CCFL extensively under real-world settings in which the FL latency is optimized
under given security requirements. Numerical results demonstrate the
significant effectiveness of the proposed approach in terms of both training
efficiency and communication security.
