Results 251 to 260 of about 31,221,148 (284)

Shallow and Deep Non-IID Learning on Complex Data

Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022
Non-IID (i.i.d.) data holds complex non-IIDness, e.g., couplings and interactions (non-independent) and heterogeneities (not IID drawn from a given distribution).
Longbing Cao, Philip S. Yu, Zhilin Zhao 0001   +2 more
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Distribution-Regularized Federated Learning on Non-IID Data

2023 IEEE 39th International Conference on Data Engineering (ICDE), 2023
Federated learning (FL) has emerged as a popular machine learning paradigm recently. Compared with traditional distributed learning, its unique challenges mainly lie in communication efficiency and non-IID (heterogeneous data) problem.
Yansheng Wang, Yongxin Tong, Zimu Zhou, Ruisheng Zhang, Sinno Jialin Pan, Lixin Fan, Qiang Yang 0001   +6 more
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Federated Learning With Non-IID Data: A Survey

IEEE Internet of Things Journal
Yueyue Dai, Yan Zhang, Heng Pan
exaly   +3 more sources

Joint User Association and Resource Allocation for Wireless Hierarchical Federated Learning With IID and Non-IID Data [PDF]

IEEE Transactions on Wireless Communications, 2022
In this work, hierarchical federated learning (HFL) over wireless multi-cell networks is proposed for large-scale model training while preserving data privacy. However, the imbalanced data distribution has a significant impact on the convergence rate and
Shengli Liu, Guanding Yu, Xianfu Chen
exaly   +2 more sources

A Clustered Federated Learning Method of User Behavior Analysis Based on Non-IID Data

Electronics, 2023
Federated learning (FL) is a novel distributed machine learning paradigm. It can protect data privacy in distributed machine learning. Hence, FL provides new ideas for user behavior analysis.
Jianfei Zhang
exaly   +2 more sources

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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