mlsanityData to 7 Oct 2026

FedComm: Federated Learning as a Medium for Covert Communication

arXiv 2201.08786 ↗First posted 21 Jan 2022cs.CR cs.LGcited by 3

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Abstract (arXiv)

Proposed as a solution to mitigate the privacy implications related to the adoption of deep learning, Federated Learning (FL) enables large numbers of participants to successfully train deep neural networks without having to reveal the actual private training data. To date, a substantial amount of research has investigated the security and privacy properties of FL, resulting in a plethora of innovative attack and defense strategies. This paper thoroughly investigates the communication capabilities of an FL scheme. In particular, we show that a party involved in the FL learning process can use FL as a covert communication medium to send an arbitrary message. We introduce FedComm, a novel multi-system covert-communication technique that enables robust sharing and transfer of targeted payloads within the FL framework. Our extensive theoretical and empirical evaluations show that FedComm provides a stealthy communication channel, with minimal disruptions to the training process. Our experiments show that FedComm successfully delivers 100% of a payload in the order of kilobits before the FL procedure converges. Our evaluation also shows that FedComm is independent of the application domain and the neural network architecture used by the underlying FL scheme.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 54 of its 134 references that are in the map. First posted on arXiv 21 Jan 2022.

Builds on

54 of 134 references.

References · 54

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38 more
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Cited by year

Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 1.

3 papers.

  1. 2022TATTOOED: A Robust Deep Neural Network Watermarking Scheme based on Spread-Spectrum Channel Codingcited by 3
  2. 2024Have You Poisoned My Data? Defending Neural Networks against Data Poisoningcited by 1
  3. 2025LLM in the Middle: A Systematic Review of Threats and Mitigations to Real-World LLM-based Systemscited by 1