5 papers
Similarity Weighted Aggregation with Global Differential Privacy for Federated Brain Lesion Segmentation
Muhammad Irfan Khan, Eero Lehtonen, Joni Obradovic +4
Federated Learning (FL) enables collaborative training of machine learning models across multiple institutions without sharing sensitive data, making it particularly suitable for m…
The MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024: Efficient and Robust Aggregation Methods for Federated Learning
Akis Linardos, Sarthak Pati, Ujjwal Baid +25
We present the design and results of the MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024, which focuses on federated learning (FL) for glioma sub-region segmentation in m…
Differential Privacy for Adaptive Weight Aggregation in Federated Tumor Segmentation
Muhammad Irfan Khan, Esa Alhoniemi, Elina Kontio +2
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 d…
Election of Collaborators via Reinforcement Learning for Federated Brain Tumor Segmentation
Muhammad Irfan Khan, Elina Kontio, Suleiman A. Khan +1
Federated learning (FL) enables collaborative model training across decentralized datasets while preserving data privacy. However, optimally selecting participating collaborators i…
Recommender Engine Driven Client Selection in Federated Brain Tumor Segmentation
Muhammad Irfan Khan, Elina Kontio, Suleiman A. Khan +1
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 2…