Privacy-Preserving Federated Learning for Crimes Against Humanity in Uganda: Robustness Under Distribution Shift and Sparse Labels
The documentation and analysis of crimes against humanity in Uganda confront a dual computational challenge: data are distributed across sensitive, institutionally siloed repositories, and the labels required for supervised learning are exceptionally sparse due to the cost and risk of expert annotation. We introduce a...