Federated Co-Training for Privacy in Sensitive Data
Concepts de base
Federated Co-Training enhances privacy in collaborative machine learning by sharing hard labels on an unlabeled dataset, improving privacy substantially while maintaining model quality.
Résumé
- Federated learning allows collaborative training without sharing sensitive data directly.
- Federated Co-Training (FEDCT) shares hard labels on an unlabeled dataset to improve privacy.
- FEDCT achieves model quality comparable to Federated Averaging (FEDAVG) and Distributed Distillation (DD) while enhancing privacy.
- FEDCT enables training of interpretable models like decision trees, XGBoost, and random forests in a federated setup.
- Empirical evaluations show FEDCT's effectiveness on various datasets and its scalability with the number of clients.
- FEDCT's impact on healthcare and chronic disease management is significant, addressing privacy concerns in collaborative training.
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Protecting Sensitive Data through Federated Co-Training
Stats
Federated learning allows us to collaboratively train models without pooling sensitive data directly.
FEDCT shares hard labels on an unlabeled dataset to improve privacy substantially.
FEDCT achieves a test accuracy comparable to FEDAVG and DD while enhancing privacy.
Citations
"Sharing hard labels substantially improves privacy over sharing model parameters."
"FEDCT achieves a model quality comparable to federated learning while improving privacy."
Questions plus approfondies
질문 1
FEDCT를 더 많은 클라이언트로 확장 가능하도록 최적화하는 방법은 무엇인가요?
Answer 1 here
질문 2
만성 질환 관리를 위해 FEDCT를 사용하는 것의 잠재적인 윤리적 영향은 무엇인가요?
Answer 2 here
질문 3
FEDCT를 의료 분야를 넘어 다른 산업에 적용하여 협력적 훈련의 개인 정보 보호를 향상시키는 방법은 무엇인가요?
Answer 3 here