Federated Co-Training for Privacy in Sensitive Data

Core Concepts

Federated Co-Training enhances privacy in collaborative machine learning by sharing hard labels on an unlabeled dataset, improving privacy substantially while maintaining model quality.

Abstract

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.

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.

Quotes

"Sharing hard labels substantially improves privacy over sharing model parameters." "FEDCT achieves a model quality comparable to federated learning while improving privacy."

Key Insights Distilled From

Protecting Sensitive Data through Federated Co-Training

by Amr Abourayy... at arxiv.org 03-05-2024

https://arxiv.org/pdf/2310.05696.pdf

Deeper Inquiries

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