Differential Privacy for Adaptive Weight Aggregation in Federated Tumor
  Segmentation
Muhammad Irfan Khan Esa Alhoniemi Elina Kontio Suleiman A. Khan Mojtaba Jafaritadi
Federated Learning (FL) is a distributed machine learning approach that
safeguards privacy by creating an impartial global model while respecting the
privacy of individual client data. However, the conventional FL method can
introduce security risks when dealing with diverse client data, potentially
compromising privacy and data integrity. To address these challenges, we
present a differential privacy (DP) federated deep learning framework in
medical image segmentation. In this paper, we extend our similarity weight
aggregation (SimAgg) method to DP-SimAgg algorithm, a differentially private
similarity-weighted aggregation algorithm for brain tumor segmentation in
multi-modal magnetic resonance imaging (MRI). Our DP-SimAgg method not only
enhances model segmentation capabilities but also provides an additional layer
of privacy preservation. Extensive benchmarking and evaluation of our
framework, with computational performance as a key consideration, demonstrate
that DP-SimAgg enables accurate and robust brain tumor segmentation while
minimizing communication costs during model training. This advancement is
crucial for preserving the privacy of medical image data and safeguarding
sensitive information. In conclusion, adding a differential privacy layer in
the global weight aggregation phase of the federated brain tumor segmentation
provides a promising solution to privacy concerns without compromising
segmentation model efficacy. By leveraging DP, we ensure the protection of
client data against adversarial attacks and malicious participants.