Privacy-Preserving Federated Learning for Digital Libraries Quarterly Focus in Uganda: Robustness Under Distribution Shift and Sparse Labels
Federated learning offers a promising architecture for digital library consortia in low-resource settings, where centralising user interaction data is often infeasible due to privacy concerns and bandwidth constraints. We introduce a client-weighted aggregation rule that corrects for covariate shift through importance...