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Balancing Privacy and Performance for Private Federated Learning Algorithms
Our findings reveal a direct correlation between the optimal number of local steps, communication rounds, and a set of variables, e. g the DP privacy budget and other problem parameters, specifically in the context of strongly convex optimization.
Weakly Unsupervised Domain Adaptation for Vestibular Schwannoma Segmentation
In this work, we propose a weakly supervised machine learning approach that learns from only ceT1 scans and adapts to segment two structures from hrT2 scans: the VS and the cochlea from the crossMoDA dataset.
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