  On-device machine learning (ML) has brought about the accessibility to a
tremendous amount of data from the users while keeping their local data private
instead of storing it in a central entity. However, for privacy guarantee, it
is inevitable at each device to compensate for the quality of data or learning
performance, especially when it has a non-IID training dataset. In this paper,
we propose a data augmentation framework using a generative model: multi-hop
federated augmentation with sample compression (MultFAug). A multi-hop protocol
speeds up the end-to-end over-the-air transmission of seed samples by enhancing
the transport capacity. The relaying devices guarantee stronger privacy
preservation as well since the origin of each seed sample is hidden in those
participants. For further privatization on the individual sample level, the
devices compress their data samples. The devices sparsify their data samples
prior to transmissions to reduce the sample size, which impacts the
communication payload. This preprocessing also strengthens the privacy of each
sample, which corresponds to the input perturbation for preserving sample
privacy. The numerical evaluations show that the proposed framework
significantly improves privacy guarantee, transmission delay, and local
training performance with adjustment to the number of hops and compression
rate.
