프라이버시 보존 머신러닝
Stub. 현재 wiki에서는 이정혜 의 contextual embedding harmonization, patient similarity, federated representation learning 라인을 묶는 연구 주제로 쓰인다.
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인접 그래프
1-hop 이웃 29개
- 인물 1
- 방법론 1
- 주제 17
- 분류 1
- 논문 9
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논문 (9)
- Bilingual autoencoder-based efficient harmonization of multi-source private data for accurate predictive modeling
- Connecting Low-Loss Subspace for Personalized Federated Learning
- Federated Gradient Boosting for Financial Fraud Detection: An Empirical Study in the Banking Sector
- HarmoSATE: Harmonized embedding-based self-attentive encoder to improve accuracy of privacy-preserving federated predictive analysis
- Privacy-Preserving Patient Similarity Learning in a Federated Environment: Development and Analysis
- Privacy-Preserving Predictive Modeling: Harmonization of Contextual Embeddings From Different Sources
- Pursuing Overall Welfare in Federated Learning through Sequential Decision Making
- Secure and Differentially Private Logistic Regression for Horizontally Distributed Data
- Word2Vec-based efficient privacy-preserving shared representation learning for federated recommendation system in a cross-device setting