Muhammad Irfan Khan

Muhammad Irfan Khan, Esa Alhoniemi, Elina Kontio et al.

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.

Muhammad Irfan Khan, Elina Kontio, Suleiman A. Khan et al.

This study presents a robust and efficient client selection protocol designed to optimize the Federated Learning (FL) process for the Federated Tumor Segmentation Challenge (FeTS 2024). In the evolving landscape of FL, the judicious selection of collaborators emerges as a critical determinant for the success and efficiency of collective learning endeavors, particularly in domains requiring high precision. This work introduces a recommender engine framework based on non-negative matrix factorization (NNMF) and a hybrid aggregation approach that blends content-based and collaborative filtering. This method intelligently analyzes historical performance, expertise, and other relevant metrics to identify the most suitable collaborators. This approach not only addresses the cold start problem where new or inactive collaborators pose selection challenges due to limited data but also significantly improves the precision and efficiency of the FL process. Additionally, we propose harmonic similarity weight aggregation (HSimAgg) for adaptive aggregation of model parameters. We utilized a dataset comprising 1,251 multi-parametric magnetic resonance imaging (mpMRI) scans from individuals diagnosed with glioblastoma (GBM) for training purposes and an additional 219 mpMRI scans for external evaluations. Our federated tumor segmentation approach achieved dice scores of 0.7298, 0.7424, and 0.8218 for enhancing tumor (ET), tumor core (TC), and whole tumor (WT) segmentation tasks respectively on the external validation set. In conclusion, this research demonstrates that selecting collaborators with expertise aligned to specific tasks, like brain tumor segmentation, improves the effectiveness of FL networks.

Muhammad Irfan Khan, Elina Kontio, Suleiman A. Khan et al.

Federated learning (FL) enables collaborative model training across decentralized datasets while preserving data privacy. However, optimally selecting participating collaborators in dynamic FL environments remains challenging. We present RL-HSimAgg, a novel reinforcement learning (RL) and similarity-weighted aggregation (simAgg) algorithm using harmonic mean to manage outlier data points. This paper proposes applying multi-armed bandit algorithms to improve collaborator selection and model generalization. By balancing exploration-exploitation trade-offs, these RL methods can promote resource-efficient training with diverse datasets. We demonstrate the effectiveness of Epsilon-greedy (EG) and upper confidence bound (UCB) algorithms for federated brain lesion segmentation. In simulation experiments on internal and external validation sets, RL-HSimAgg with UCB collaborator outperformed the EG method across all metrics, achieving higher Dice scores for Enhancing Tumor (0.7334 vs 0.6797), Tumor Core (0.7432 vs 0.6821), and Whole Tumor (0.8252 vs 0.7931) segmentation. Therefore, for the Federated Tumor Segmentation Challenge (FeTS 2024), we consider UCB as our primary client selection approach in federated Glioblastoma lesion segmentation of multi-modal MRIs. In conclusion, our research demonstrates that RL-based collaborator management, e.g. using UCB, can potentially improve model robustness and flexibility in distributed learning environments, particularly in domains like brain tumor segmentation.