Current Result Document :
| 한글제목(Korean Title) |
DRM-FL: Cross-Silo Federated Learning 접근법의 프라이버시 보호를 위한 분산형 랜덤화 메커니즘 |
| 영문제목(English Title) |
DRM-FL: A Decentralized and Randomized Mechanism for Privacy Protection in Cross-Silo Federated Learning Approach |
| 저자(Author) |
무함마드 필다우스
초느에진랏
마리즈아길랄
이경현
Muhammad Firdaus
Cho Nwe Zin Latt
Mariz Aguilar
Kyung-Hyune Rhee
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| 원문수록처(Citation) |
VOL 29 NO. 01 PP. 0264 ~ 0267 (2022. 05) |
한글내용
(Korean Abstract) |
Recently, federated learning (FL) has increased prominence as a viable approach for enhancing user privacy and data security by allowing collaborative multi-party model learning without exchanging sensitive data. Despite this, most present FL systems still depend on a centralized aggregator to generate a global model by gathering all submitted models from users, which could expose user privacy and the risk of various threats from malicious users. To solve these issues, we suggested a safe FL framework that employs differential privacy to counter membership inference attacks during the collaborative FL model training process and empowers blockchain to replace the centralized aggregator server. |
영문내용
(English Abstract) |
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| 키워드(Keyword) |
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| 파일첨부 |
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