Differential Privacy for Adaptive Weight Aggregation in Federated Tumor Segmentation. (arXiv:2308.00856v1 [cs.LG])

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 …

aggregation client data data integrity differential privacy distributed federated learning global integrity machine machine learning privacy risks safeguards security security risks segmentation