Data heterogeneity and personalization in federated learning
J.A. Scott, Data Heterogeneity and Personalization in Federated Learning, Institute of Science and Technology Austria, 2026.

Differentially private federated k-means clustering with server-side data
J.A. Scott, C. Lampert, D. Saulpic, in:, 42nd International Conference on Machine Learning, ML Research Press, 2025, pp. 53757–53790.

Federated learning with unlabeled clients: Personalization can happen in low dimensions
H. Zakerinia, J.A. Scott, C. Lampert, ArXiv (n.d.).

PEFLL: Personalized federated learning by learning to learn
J.A. Scott, H. Zakerinia, C. Lampert, in:, 12th International Conference on Learning Representations, OpenReview, 2024.

Improved modelling of federated datasets using mixtures-of-Dirichlet-multinomials
J.A. Scott, Á. Cahill, in:, Proceedings of the 41st International Conference on Machine Learning, ML Research Press, 2024, pp. 44012–44037.

Cross-client label propagation for transductive and semi-supervised federated learning
J.A. Scott, M.X. Yeo, C. Lampert, in:, Transactions in Machine Learning, Curran Associates, 2023.