Results 101 to 110 of about 72,357 (237)

Uldp-FL: Federated Learning with Across-Silo User-Level Differential Privacy

Proceedings of the VLDB Endowment
Differentially Private Federated Learning (DP-FL) has garnered attention as a collaborative machine learning approach that ensures formal privacy. Most DP-FL approaches ensure DP at the record-level within each silo for cross-silo FL. However, a single user's data may extend across multiple silos, and the desired user-level DP guarantee for such a ...
Kato, Fumiyuki, Xiong, Li, Takagi, Shun, Cao, Yang, Yoshikawa, Masatoshi   +4 more
openaire   +3 more sources

AI and Big Data in Oncology: A Physician‐Centered Perspective on Emerging Clinical and Research Applications

Cancer Innovation, Volume 5, Issue 1, February 2026.
Artificial intelligence and big data platforms are transforming oncology clinical practice. This review proposes a physician‐centered framework to integrate AI tools with real‐world data, supporting more precise diagnosis, individualized treatment, and improved patient outcomes.
Binliang Liu, Qingyao Shang, Jun Li, Shuna Yao, Meishuo Ouyang, Yu Wang, Sheng Luo, Quchang Ouyang   +7 more
wiley   +1 more source

Federated target trial emulation using distributed observational data for treatment effect estimation

npj Digital Medicine
Target trial emulation (TTE) aims to estimate treatment effects by simulating randomized controlled trials using real-world observational data. Applying TTE across distributed datasets shows great promise in improving generalizability and power but is ...
Haoyang Li, Chengxi Zang, Zhenxing Xu, Weishen Pan, Suraj Rajendran, Yong Chen, Fei Wang   +6 more
doaj   +1 more source

iCARDIO Alliance Global Implementation Guidelines for the Management of Obesity 2025

Journal of Cachexia, Sarcopenia and Muscle, Volume 17, Issue 1, February 2026.
ABSTRACT There are a number of guidelines on how to manage obesity, but inconsistencies in healthcare access, varying infrastructure, resource constraints and diverse local practices restrict their global applicability. This underscores the need for universal recommendations that address the unique challenges faced by patients and healthcare providers ...
Stefan D. Anker, Linong Ji, Tammy Kindel, Andrew J. S. Coats, Dike Ojji, Adriana Puente Barragán, Peter Rossing, Shelley Zieroth, Shaaf Ahmad, Shariq Usman, Geeta Appannah, Alison L. Bailey, Ahmed Bennis, Andrea Brandao, Javed Butler, Melanie J. Davies, Lubomira Fabryova, Yuan‐Lin Guo, Hidetaka Itoh, Uday M. Jadhav, Carel W. Le Roux, Fausto J. Pinto, Julio Rosenstock, Banshi Saboo, Hani Sabbour, Mangesh Tiwaskar, Karol E. Watson, Kwang Wei Tham, Fernando Stuardo Wyss, Walter P. Abhayaratna, William T. Abraham, Wael Al Mahmeed, Alessia Argirò, John J. Atherton, Danielle Belardo, Raquel Campuzano, Nandini Chatterjee, Marc‐André Cornier, Sarah Davies, Clemencia de Rueda Panadero, Anastase Dzudie, Ty J. Gluckman, Muhammad Shahzeb Khan, Kamlesh Khunti, Yuri Lopatin, Zhiyi Ma, Okechukwu S. Ogah, Abraham Oomman, Emilio S. Peralta Lopez, Ping Li, Paul Poirier, Julie Redfern, Giuseppe M. C. Rosano, Donna Ryan, Amit Saraf, Sameh Shaheen, Subodh Verma, Stephan von Haehling, Martha Gulati, Naveed Sattar, Jose Luis Zamorano   +60 more
wiley   +1 more source

A Federated Learning Benchmark on Tabular Data: Comparing Tree-Based Models and Neural Networks

Federated Learning (FL) has lately gained traction as it addresses how machine learning models train on distributed datasets. FL was designed for parametric models, namely Deep Neural Networks (DNNs).Thus, it has shown promise on image and text tasks ...
Lindskog, William, Prehofer, Christian
core   +1 more source

Privacy preservation for federated learning in health care

Patterns
Summary Artificial intelligence (AI) shows potential to improve health care by leveraging data to build models that can inform clinical workflows. However, access to large quantities of diverse data is needed to develop robust generalizable models.
Sarthak Pati, Sourav Kumar, Amokh Varma, Brandon Edwards, Charles Lu, Liangqiong Qu, Justin J. Wang, Anantharaman Lakshminarayanan, Shih-han Wang, Micah J. Sheller, Ken Chang, Praveer Singh, Daniel L. Rubin, Jayashree Kalpathy-Cramer, S. Bakas   +14 more
semanticscholar   +1 more source

A New Method for Probabilistic Spatiotemporal Forecasts of Solar Soft X‐Ray “S‐Class” (>X10) Superflares

Journal of Geophysical Research: Space Physics, Volume 131, Issue 2, February 2026.
Abstract Solar superflares of S‐class (>X10 in soft X‐rays) pose extreme space weather hazards, yet their prediction remains a fundamental challenge owing to their rapid and transient natures and the limitations of conventional event‐based forecasts. We introduce for the first time, a probabilistic spatiotemporal framework designed to identify extended
V. M. Velasco Herrera, G. Velasco Herrera, W. Soon, A. Özgüç, N. Babynets, A. Tlatov, M. Švanda, S. Qiu, S. Baliunas, B. Kotan, G. González González, L. A. Bautista Flores, M. Pazos   +12 more
wiley   +1 more source

Federated Learning Incentive Mechanism with Supervised Fuzzy Shapley Value

Axioms
The distributed training of federated machine learning, referred to as federated learning (FL), is discussed in models by multiple participants using local data without compromising data privacy and violating laws. In this paper, we consider the training
Xun Yang, Shuwen Xiang, Changgen Peng, Weijie Tan, Yue Wang, Hai Liu, Hongfa Ding   +6 more
doaj   +1 more source

JoVe-FL: A Joint-Embedding Vertical Federated Learning Framework

Proceedings of the 15th International Conference on Agents and Artificial Intelligence, 2023
Hartmann, Lena Maria, Danoy, Grégoire, Alswaitti, Mohammed, Bouvry, Pascal   +3 more
openaire   +2 more sources

FMLD: A Vertical Federated Learning Framework for Privacy‐Preserving Multimodal Landslide Detection

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 1, February 2026.
Abstract Landslides are among the most severe global geohazards posing a significant threat to human life and infrastructure. To support landslide detection and prediction, various geohazard monitoring approaches have been developed, such as optical remote sensing imagery, light detection and ranging, and ground‐based sensors, generating vast volumes ...
Xiaochuan Tang, Zhong Lu, Xuanmei Fan, Xiaochuang Yan, Huailiang Li, Shui‐Hua Jiang, Sansar Raj Meena, Dongfen Li, Kushanav Bhuyan, Alessandro Novellino, Hongjun Li, Filippo Catani   +11 more
wiley   +1 more source