E. Jeong, S. Oh, J. Park, H. Kim, M. Bennis and S. -L. Kim, "Hiding in the Crowd: Federated Data Augmentation for On-Device Learning," in IEEE Intelligent Systems, vol. 36, no. 5, pp. 80-87, 1 Sept.-Oct. 2021, doi: 10.1109/MIS.2020.3028613
Hiding in the crowd : federated data augmentation for on-device learning
|Author:||Jeong, Eunjeong1; Oh, Seungeun1; Park, Jihong2;|
1Yonsei University, Seoul, Korea
2Deakin University, Victoria, Australia
3Samsung Research, Seoul, Korea
4University of Oulu, Oulu, Finland
|Online Access:||PDF Full Text (PDF, 1 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2021122162702
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2021-12-21
To cope with the lack of on-device machine learning samples, this article presents a distributed data augmentation algorithm, coined federated data augmentation (FAug). In FAug, devices share a tiny fraction of their local data, i.e., seed samples, and collectively train a synthetic sample generator that can augment the local datasets of devices. To further improve FAug, we introduce a multihop-based seed sample collection method and an oversampling technique that mixes up collected seed samples. Both approaches enjoy the benefit from the crowd of devices, by hiding data privacy from preceding hops and feeding diverse seed samples. In the image classification tasks, simulations demonstrate that the proposed FAug frameworks yield stronger privacy guarantees, lower communication latency, and higher on-device ML accuracy.
IEEE intelligent systems
|Pages:||80 - 87|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This research was supported by a grant to Bio-Mimetic Robot Research Center Funded by Defense Acquisition Program Administration, and by Agency for Defense Development (UD190018ID).
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.