FedComm: Federated Learning as a Medium for Covert Communication

Abstract

Proposed as a solution to mitigate the privacy implications related to the adoption of deep learning solutions, 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 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, allowed us to successfully deliver 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.

TATTOOED: A Robust Deep Neural Network Watermarking Scheme based on Spread-Spectrum Channel Coding

“…There is little amount of work that couples spread-spectrum channel coding with machine learning, and particularly deep learning applications. Prior work uses such techniques to build a covert communication channel on top of federated learning schemes [23], a topic which is different to the one discussed in this paper. To the best of our knowledge, this is the first work that makes use of spread-spectrum channel coding for watermarking DNN models.…”