Results 231 to 240 of about 109,032 (273)

Edge computing with federated learning for early detection of citric acid overdose and adjustment of regional citrate anticoagulation. [PDF]

BMC Med Inform Decis Mak
Mali S, Mali N, Zeng F, Zhang L.
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The third study of infectious intestinal disease in the community microbiological methods

Bronowski C, Hughes A, Jenkins C, Godbole G, Chalmers R, Celma C, Beard S, McDowell M, Oates M, Elwin K, Cunningham-Oakes E, Darby A, Hungerford D, Mill A, O’Brien S, Hopkins M, Cunliffe N, the IID3 Consortium.   +17 more
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Exploring personalization via federated representation Learning on non-IID data

Neural Networks, 2023
Federated Learning (FL) can learn a global model across decentralized data over different clients. However, it is susceptible to statistical heterogeneity of client-specific data. Clients focus on optimizing for their individual target distributions, which would yield divergence of the global model due to inconsistent data distributions.
Changxing Jing, Yan Huang, Yihong Zhuang, Liyan Sun, Zhenlong Xiao, Yue Huang, Xinghao Ding   +6 more
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Non-IIDness Learning in Behavioral and Social Data

The Computer Journal, 2013
Most of the classic theoretical systems and tools in statistics, data mining and machine learning are built on the fundamental assumption of IIDness, which assumes the independence and identical distribution of underlying objects, attributes and/or values.
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Efficient Split Learning with Non-iid Data

2022 23rd IEEE International Conference on Mobile Data Management (MDM), 2022
Yuanqin Cai, Tongquan Wei
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Federated Dictionary Learning from Non-IID Data

2022 IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP), 2022
Alexandros Gkillas, Dimitris Ampeliotis, Kostas Berberidis   +2 more
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Non-IID federated learning with Mixed-Data Calibration

Applied and Computational Engineering
Federated learning (FL) is a privacy-preserving and collaborative machine learning approach for decentralized data across multiple clients. However, the presence of non-independent and non-identically distributed (non-IID) data among clients poses challenges to the performance of the global model.
Xufei Zhang, Yiqing Shen
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Private Data Synthesis from Decentralized Non-IID Data

2023 International Joint Conference on Neural Networks (IJCNN), 2023
Muhammad Usama Saleem, Liyue Fan
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FedEL: Federated ensemble learning for non-iid data

Expert Systems with Applications
Federated learning (FL) is a joint training pattern that fully utilizes data information whereas protecting data privacy. A key challenge in FL is statistical heterogeneity, which arises on account of the heterogeneity of local data distributions among clients, leading to inconsistency in local optimization goals and ultimately reducing the performance
Xing Wu, Jie Pei, Xian-Hua Han, Yen-Wei Chen, Junfeng Yao, Yang Liu, Quan Qian, Yike Guo   +7 more
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Communication-Efficient Federated Data Augmentation on Non-IID Data

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2022
Hui Wen, Yue Wu, Jingjing Li, Hancong Duan   +3 more
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