DAG-Based Blockchain Sharding for Secure Federated Learning with Non-IID Dataopen access
- Authors
- Lee, Jungjae; Kim, Wooseong
- Issue Date
- Nov-2022
- Publisher
- MDPI
- Keywords
- blockchain; federated learning; smart contract; model-poisoning attack
- Citation
- SENSORS, v.22, no.21
- Journal Title
- SENSORS
- Volume
- 22
- Number
- 21
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/86263
- DOI
- 10.3390/s22218263
- ISSN
- 1424-8220
- Abstract
- Federated learning is a type of privacy-preserving, collaborative machine learning. Instead of sharing raw data, the federated learning process cooperatively exchanges the model parameters and aggregates them in a decentralized manner through multiple users. In this study, we designed and implemented a hierarchical blockchain system using a public blockchain for a federated learning process without a trusted curator. This prevents model-poisoning attacks and provides secure updates of a global model. We conducted a comprehensive empirical study to characterize the performance of federated learning in our testbed and identify potential performance bottlenecks, thereby gaining a better understanding of the system.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - IT융합대학 > 컴퓨터공학과 > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/86263)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.