Similarity Weighted Aggregation with Global Differential Privacy for Federated Brain Lesion Segmentation

Federated Learning (FL) enables collaborative training of machine learning models across multiple institutions without sharing sensitive data, making it particularly suitable for medical imaging applications. However, heterogeneous data distributions across institutions and potential information leakage through model updates remain important challenges. In this work, we propose DP-SimAgg, a privacy-preserving federated learning framework that integrates similarity-weighted aggregation with a ser

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ethics.ai (1 August 2026), “Similarity Weighted Aggregation with Global Differential Privacy for Federated Brain Lesion Segmentation,” evidence record 16138, https://ethics.ai/record/16138 (originally published by arXiv cs.CR (AI security)).

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