Federated learning (FL) routinely exchanges model-derived signals (e.g., logits or updates) between clients and a server, creating an attractive substrate for covert communication — especially in settings where adversaries cannot rely on direct, out-of-band coordination. Existing FL covert channels often trade off capacity, reliability under aggregation, setup requirements, or operational stealth (e.g., needing extensive pre-shared state, warm-up rounds, or leaving persistent artifacts).

While Machine Learning (ML) is one of the most promising technologies in our era, it is prone to a variety of attacks. One of them is covert channels, that enable two parties to stealthily transmit information through carriers intended for different purposes. Existing works only explore covert channels for federated ML. Thereby, communication is established among multiple entities that collaborate to train a model, while relying on access to model internals.

Federated Learning (FL) enhances decentralized machine learning by safeguarding data privacy, reducing communication costs, and improving model performance with diverse data sources. However, FL faces vulnerabilities such as untargeted poisoning attacks and targeted backdoor attacks, posing challenges to model integrity and security. Preventing backdoors proves especially challenging due to their stealthy nature. Existing mitigation techniques have shown efficacy but often overlook realistic adversaries and diverse data distributions.