Protecting Sensitive Data through Federated Co-Training: Privacy and Model Quality Trade-Off

Conceptos Básicos

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.

Resumen

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:

1. Introduction to Federated Co-Training for Privacy Protection.
2. Comparison with existing methods like FEDAVG and DP-FEDAVG.
3. Empirical evaluations on benchmark datasets and real-world medical datasets.
4. Scalability analysis with varying numbers of clients.
5. Impact statements emphasizing the significance of privacy in collaborative training, particularly in healthcare applications.

Estadísticas

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.

Citas

"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."

Ideas clave extraídas de

Protecting Sensitive Data through Federated Co-Training

by Amr Abourayy... a las arxiv.org 03-05-2024

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

Consultas más profundas

How can federated co-training be applied beyond machine learning contexts

Federated co-training can be applied beyond machine learning contexts in various fields where collaborative training is necessary but data privacy is a concern. For example, in healthcare, federated co-training can be used to train predictive models for disease diagnosis or treatment recommendations without sharing sensitive patient data directly. This approach allows multiple healthcare institutions to collaborate and benefit from each other's datasets while maintaining the privacy of individual patients' information. Additionally, federated co-training can be applied in financial services for fraud detection, where different banks or financial institutions can collaborate on building robust fraud detection models without compromising customer data security.

What are potential drawbacks or limitations of using hard label sharing for improved privacy

While hard label sharing improves privacy substantially in federated co-training by reducing the risk of inference attacks on sensitive data, there are potential drawbacks and limitations to consider. One limitation is that hard labels may not capture the full complexity of the underlying data distribution as effectively as soft labels. Hard labels are discrete and may lead to loss of information compared to soft labels which provide more nuanced probabilistic information about class predictions. Additionally, using hard labels for improved privacy may limit the types of machine learning algorithms that can be effectively utilized since some algorithms require continuous output values rather than discrete class labels.

How might differential privacy impact the scalability and efficiency of federated co-training

Differential privacy could impact the scalability and efficiency of federated co-training by introducing additional computational overhead due to the randomization process required for ensuring differential privacy guarantees. The introduction of noise or randomness into shared information to achieve differential privacy may increase communication costs between clients and servers during training iterations. This added complexity could potentially slow down convergence rates and increase overall training time in a federated setting. However, optimizing differential privacy mechanisms tailored specifically for federated co-training scenarios could help mitigate these scalability challenges and improve efficiency without compromising on data privacy protections.