Privacy-preserving Federated Brain Tumour Segmentation (1910.00962v1)

Abstract: Due to medical data privacy regulations, it is often infeasible to collect and share patient data in a centralised data lake. This poses challenges for training machine learning algorithms, such as deep convolutional networks, which often require large numbers of diverse training examples. Federated learning sidesteps this difficulty by bringing code to the patient data owners and only sharing intermediate model training updates among them. Although a high-accuracy model could be achieved by appropriately aggregating these model updates, the model shared could indirectly leak the local training examples. In this paper, we investigate the feasibility of applying differential-privacy techniques to protect the patient data in a federated learning setup. We implement and evaluate practical federated learning systems for brain tumour segmentation on the BraTS dataset. The experimental results show that there is a trade-off between model performance and privacy protection costs.

• The paper introduces a robust federated learning system that enables collaborative brain tumour segmentation without centralized data storage.
• The integration of differential privacy through noise infusion in client updates safeguards sensitive patient data from inference attacks.
• Empirical validation on the BraTS dataset demonstrates that the framework attains segmentation accuracy comparable to conventional centralized methods.

Privacy-preserving Federated Brain Tumour Segmentation: An Overview

The paper "Privacy-preserving Federated Brain Tumour Segmentation" provides an incisive paper into the utilization of federated learning (FL) frameworks combined with differential privacy techniques to address the complexities of brain tumor segmentation in medical imaging. Given the proliferation of privacy regulations that impede the collation and centralized storage of medical data, federated learning emerges as a viable solution to train deep neural networks by allowing multiple institutions to collaboratively enhance model performance without direct data sharing.

Core Contributions and Experimental Evaluation

1. Federated Learning Framework: The paper sets forth a robust federated learning system aimed at performing medical image analysis specifically for brain tumor segmentation. It employs a client-server architecture, wherein different clients train local models using stochastic gradient descent (SGD), followed by periodic aggregation of locally computed models at a central server to update a global model. Implementing this distributed setup, the paper enforces local data privacy while enabling collaborative learning across distinct data sources.
2. Differential Privacy Assurance: Despite the inherent data security in federated learning, there is a non-negligible risk of model inversion attacks compromising privacy. To mitigate this, the authors integrate differential privacy mechanisms into the FL setup. The privacy-preserving model modifies client-side updates by introducing noise through selective parameter sharing, enhancing the resilience of sensitive patient data against inference attacks.
3. Empirical Analysis on BraTS Dataset: Validation of the proposed framework was executed using the BraTS 2018 dataset, comprising multi-parametric MRI scans of 285 subjects. The authors delineate the system's capability to achieve segmentation results comparable to centralized training methods, demonstrating efficacy in handling diverse and imbalanced data across institutional boundaries.

Key Insights from Results

• The paper reveals a notable balance between maintaining model accuracy and upholding privacy constraints. By selectively sharing gradient updates and introducing differential privacy through noise infusion, the model achieves significant performance whilst adhering to strict privacy norms.
• Innovations such as momentum restarting and weighted averaging during federated aggregation reflect sophisticated strategies to refine convergence rates and optimize performance, especially in heterogeneously distributed datasets.
• The exploration of partial model sharing elucidates the trade-offs in information disclosure and model utility. A gradient clipping strategy, coupled with optimal noise calibration, is presented as pivotal for managing differential privacy budgets without substantial compromise to model accuracy.

Implications and Future Directions

This research effectively demonstrates the practicality of deploying privacy-preserving federated learning in sensitive domains like medical imaging. The capacity to harness multi-institutional data without direct exchange paves the way for advancements in personalized medicine and enhanced diagnostic tools, whilst ensuring patient confidentiality.

Future endeavors might extend this work by adopting differentially private stochastic gradient descent techniques, expanding privacy constructs, and optimizing resource allocation for more granular control over privacy and performance. Additionally, investigating real-world applications and scalability of this federated privacy framework could establish comprehensive insights into its operational viability and impact.

In conclusion, this paper offers a substantive contribution to the field of privacy-enhancing technologies in machine learning, underscoring federated learning's potential to transcend traditional limitations in medical data analysis.