Centrala begrepp
The author proposes a federated co-training approach to collaboratively train models while improving privacy substantially. This method achieves a favorable privacy-utility trade-off compared to traditional federated learning methods.
Sammanfattning
The content discusses the challenges of protecting sensitive data in collaborative machine learning. It introduces the concept of federated co-training as a solution to improve privacy while maintaining model quality. The approach involves sharing hard labels on an unlabeled dataset, showing significant improvements in privacy over existing methods like FEDAVG and DP-FEDAVG. The article provides theoretical analysis, empirical evaluations, and impact statements on the potential applications in healthcare.
The authors highlight the importance of privacy in collaborative training, especially in healthcare settings where access to diverse patient data is crucial for developing robust models. By introducing federated co-training, they address the privacy concerns associated with sharing sensitive information across multiple institutions. The research demonstrates how this approach can unlock machine learning potential on large distributed datasets without compromising data privacy.
Key points include:
- Introduction to Federated Co-Training for Privacy Protection.
- Comparison with existing methods like FEDAVG and DP-FEDAVG.
- Empirical evaluations on benchmark datasets and real-world medical datasets.
- Scalability analysis with varying numbers of clients.
- Impact statements emphasizing the significance of privacy in collaborative training, particularly in healthcare applications.
Statistik
Fig 1 shows that FEDCT reduces vulnerability to membership inference attacks substantially over FEDAVG while maintaining similar model quality.
DP-FEDAVG improves privacy slightly at the cost of model quality.
Sensitivity bound for differentially private FEDCT retains high model quality with strong DP guarantees.
Citat
"Sharing hard labels substantially improves privacy over sharing model parameters."
"Federated co-training achieves a model quality comparable to federated learning."
"FEDCT protects privacy almost optimally while achieving a model quality similar to FEDAVG."