| Leaf: A benchmark for federated settings S Caldas, SMK Duddu, P Wu, T Li, J Konečnư, HB McMahan, V Smith, ... arXiv preprint arXiv:1812.01097, 2018 | 1437 | 2018 |
| Expanding the reach of federated learning by reducing client resource requirements S Caldas, J Konečny, HB McMahan, A Talwalkar arXiv preprint arXiv:1812.07210, 2018 | 476 | 2018 |
| Differentially private meta-learning J Li, M Khodak, S Caldas, A Talwalkar arXiv preprint arXiv:1909.05830, 2019 | 143 | 2019 |
| Federated kernelized multi-task learning S Caldas, V Smith, A Talwalkar Proc. SysML Conf., 1-3, 2018 | 40 | 2018 |
| Understanding Clinical Collaborations Through Federated Classifier Selection S Caldas, JH Yoon, MR Pinsky, G Clermont, A Dubrawski Machine Learning for Healthcare Conference, 126-145, 2021 | 5 | 2021 |
| A case for federated learning: Enabling and leveraging inter-hospital collaboration S Caldas, V Jeanselme, G Clermont, MR Pinsky, A Dubrawski C33. QUALITY, PROCESSES, AND OUTCOMES IN ACUTE AND CRITICAL CARE, A4790-A4790, 2020 | 5 | 2020 |
| Using Machine Learning to Support Transfer of Best Practices in Healthcare S Caldas, J Chen, A Dubrawski AMIA Annual Symposium Proceedings 2021, 265, 2021 | 4 | 2021 |
| PICSR: Prototype-Informed Cross-Silo Router for Federated Learning (Student Abstract) E Enouen, S Caldas, M Goswami, A Dubrawski Proceedings of the AAAI Conference on Artificial Intelligence 38 (21), 23482 …, 2024 | | 2024 |
| Encoding Expert Knowledge into Federated Learning using Weak Supervision S Caldas, M Goswami, A Dubrawski | | |
