arXiv:2606. 23871v1 Announce Type: new Abstract: Survival analysis is central to clinical decision-making, yet reliable time-to-event models require large, diverse cohorts that are rarely available at a single institution, while privacy regulations restrict the centralization of patient data.
By Natalia Moreno-Blasco, Anusha Ihalapathirana, Pekka Siirtola, Miguel Fernandez-de-Retana
arXiv:2607. 04085v1 Announce Type: cross Abstract: Routing-prediction federated learning has emerged as a new paradigm that reframes inter-client heterogeneity as a resource for system-level intelligence: at inference time, the server routes each external query to the best-matched client for prediction.
By Zijian Wang, Pengfei Li, Guangyu Yang, Qiong Zhang
The paper introduces a federated inference algorithm that requires only a single communication round between participating centers and a coordinating server. By extending previous second‑order Taylor expansion methods to third‑order expansions, the algorithm more accurately approximates local log‑likelihood functions, especially when local sample sizes are small. Simulation studies based on real data show that this higher‑order approach improves inference accuracy while maintaining privacy, communication efficiency, and scalability for collaborative biomedical and epidemiological research.
By Laura Montagnani, Anthony CC Coolen, Marianne A Jonker
arXiv:2608. 03498v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative model training across distributed healthcare institutions without centralising sensitive patient data.
By Rojalini Tripathy, Padmalochan Bera, Shreya Ghosh, Rajkumar Buyya
arXiv:2606. 04338v1 Announce Type: new Abstract: Privacy-sensitive and distributed characteristics of multi-center medical data bring severe obstacles to centralized modeling for accurate early prediction of sepsis.
By Xixi Tian, Di Wu, Xiang Liu, Yiziting Zhu, Yujie Li, Xin Shu, Bin Yi
FedPS is a federated preprocessing framework that uses aggregated statistics to address missing values, inconsistent formats, and heterogeneous feature scales in structured data. It employs data-sketching techniques to summarize local datasets efficiently, enabling federated algorithms for feature scaling, encoding, discretization, and missing-value imputation. The framework also extends preprocessing-related models, such as Bayesian Linear Regression, to both horizontal and vertical federated learning settings, offering communication‑efficient and consistent pipelines for practical deployments.
By Xuefeng Xu, Graham Cormode