Results 251 to 260 of about 7,049,980 (289)

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, Chenyuan Feng, Jiamo Jiang   +2 more
exaly   +2 more sources

Federated User Clustering for non-IID Federated Learning

Electron. Commun. Eur. Assoc. Softw. Sci. Technol., 2022
Federated Learning (FL) is one of the leading learning paradigms for enabling a more significant presence of intelligent applications in networking considering highly distributed environments while preserving user privacy.
Lucas Pacheco, Denis Rosário, Eduadro Cerqueira, Torsten Braun   +3 more
openaire   +3 more sources

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
openaire   +2 more sources

Beyond i.i.d.: Non-IID Thinking, Informatics, and Learning

IEEE Intelligent Systems, 2022
In science, technology, engineering, and their applications, a ubiquitous assumption is independent and identically distributed (i.i.d. or IID).
Cao, L
openaire   +2 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, Mehdi Bennis   +2 more
exaly   +2 more sources

Federated Learning With Non-IID Data: A Survey

IEEE Internet of Things Journal
Heng Pan, Yueyue Dai, Yan Zhang
exaly   +2 more sources

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

Electronics (Switzerland), 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

Coefficient regularized regression with non-iid sampling

International Journal of Computer Mathematics, 2011
In this paper, we study a more general kernel regression learning with coefficient regularization. A non-iid setting is considered, where the sequence of probability measures for sampling is not identical but the sequence of marginal distributions for sampling converges exponentially fast in the dual of a Holder space; the sampling zi, i ≥ 1 are weakly
Hongwei Sun, Qin Guo
openaire   +1 more source

Introduction to the Non-IID Case

2004
We present in the following some examples to motivate the extension of the classical extreme value theory for iid sequences to a theory for non iid sequences. We introduce different classes of non iid sequences together with the main ideas. The examples show that suitable restrictions for each class are needed to find limit results which are useful for
Michael Falk, Rolf-Dieter Reiss, Jürg Hüsler   +2 more
openaire   +1 more source