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Federated Learning for non-IID Healthcare Data

International Research Journal of Modernization in Engineering Technology & Science
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Federated Learning With Non-IID Data in Wireless Networks

IEEE Transactions on Wireless Communications, 2022
Federated learning provides a promising paradigm to enable network edge intelligence in the future sixth generation (6G) systems. However, due to the high dynamics of wireless circumstances and user behavior, the collected training data is non-independent and identically distributed (non-IID), which causes severe performance degradation of federated ...
Zhongyuan Zhao 0001, Chenyuan Feng, Wei Hong 0002, Jiamo Jiang, Chao Jia, Tony Q. S. Quek, Mugen Peng   +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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Kernel Measures of Independence for non-iid Data.

2009
Many machine learning algorithms can be formulated in the framework of statistical independence such as the Hilbert Schmidt Independence Criterion. In this paper, we extend this criterion to deal with structured and interdependent observations. This is achieved by modeling the structures using undirected graphical models and comparing the Hilbert space
Zhang, X., Song, L., Gretton, A., Smola, A.   +3 more
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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 0001, Jie Pei, Xian-Hua Han, Yen-Wei Chen 0001, Junfeng Yao, Yang Liu 0005, Quan Qian, Yike Guo   +7 more
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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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Communication-Efficient Federated Data Augmentation on Non-IID Data

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2022
Hui Wen 0005, Yue Wu, Jingjing Li 0001, Hancong Duan   +3 more
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