지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2019.11
- 수록면
- 401 - 410 (10page)
이용수
초록· 키워드
오류제보하기
In clinical research, the lack of events of interest often necessitates imbalanced learning. One approach to resolve this obstacle is data integration or sharing, but due to privacy concerns neither is practical. Therefore, there is an increasing demand for a platform on which an analysis can be performed in a federated environment while maintaining privacy. However, it is quite challenging to develop a federated learning algorithm that can address both privacypreserving and class imbalanced issues. In this study, we introduce a federated generative model learning platform for generating samples in a data-distributed environment while preserving privacy. We specifically propose approximate Bayesian computation-based Gaussian Mixture Model called ‘Federated ABCGMM’, which can oversample data in a minor class by estimating the posterior distribution of model parameters across institutions in a privacy-preserving manner. PhysioNet2012, a dataset for prediction of mortality of patients in an Intensive Care Unit (ICU), was used to verify the performance of the proposed method. Experimental results show that our method boosts classification performance in terms of F1 score up to nearly an ideal situation. It is believed that the proposed method can be a novel alternative to solving class imbalance problems.
목차
Abstract
1. Introduction
2. Preliminaries
3. Methods
4. Experiments and Results
5. Discussion
6. Conclusion
References
참고문헌 (0)
참고문헌 신청함께 읽어보면 좋을 논문
논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!
-
Flower 프레임워크를 사용한 연합 학습 성능 분석
대한전자공학회 학술대회
2023 .06
-
Federated Learning for Cellular Networks: Joint User Association and Resource Allocation
한국통신학회 APNOMS
2020 .09
-
A Review of Secure Healthcare Data Analytics using Federated Machine Learning and Blockchain Technology
IEIE Transactions on Smart Processing & Computing
2024 .06
-
Differentially Private and Secure Distributed Logistic Regression based on Preconditioned Stochastic Gradient Langevin Dynamics
대한산업공학회 추계학술대회 논문집
2019 .11
-
A Study on Privacy Issues and Solutions of Public Data in Education
International journal of internet, broadcasting and communication : IJIBC
2020 .01
-
Cross-Silo Horizontal Federated Learning for Flow-based Time-related-Features Oriented Traffic Classification
한국통신학회 APNOMS
2020 .09
-
Bi-level Distillation for On-Device Learning
한국정보과학회 학술발표논문집
2021 .06
-
Federated Distillation with Adaptive Stochastic Optimization for On-Device Learning
한국정보과학회 학술발표논문집
2021 .12
-
Efficient Privacy-preserving Shared Representation Learning in Cross-device Federated Learning
대한산업공학회 춘계공동학술대회 논문집
2022 .06
-
하둡 분산 환경 기반 프라이버시 보호 빅 데이터 배포 시스템 개발
멀티미디어학회논문지
2017 .11
-
How do multilevel privacy controls affect utility‐privacy trade‐offs when used in mobile applications?
[ETRI] ETRI Journal
2018 .12
-
Federated Learning Based Energy Demand Prediction of Individual Households
한국정보과학회 학술발표논문집
2020 .07
-
대규모 연합학습을 위한 효율적 분산 클러스터링 기법
한국통신학회논문지
2022 .01
-
Privacy-Preserving Federated Learning-based DDoS Classification for IIoT Networks
한국통신학회 학술대회논문집
2022 .06
-
Forecasting Building Energy Consumption with Bayesian Optimized Model Architecture on Federated Data
한국정보과학회 학술발표논문집
2021 .06
-
데이터 분포 불균형 문제 해결을 위한 다중 기기 연합학습 기반 운전자 이상 행동 탐지
한국통신학회논문지
2025 .03
-
Development of a Federated Patient Similarity Learning Framework with Privacy Protection
대한산업공학회 추계학술대회 논문집
2016 .11
-
IoT 침입 탐지 시스템을 위하여 기계학습 분류자를 사용한 블록체인 기반 연합학습
멀티미디어학회논문지
2024 .09
-
개인정보 취급방침의 인지가 개인정보보호 행동에 미치는 영향 : 카카오톡 이용자를 중심으로
한국전자거래학회지
2016 .05
-
Plant Disease Classification with Federated Learning and Unsupervised Contrastive Pre-training
한국정보과학회 학술발표논문집
2021 .12
이 논문의 저자 정보
최근 본 자료
댓글(0)
0
UCI(KEPA) : I410-ECN-0101-2019-530-001293607