Results 251 to 260 of about 1,704,441 (287)

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

Adaptive Federated Learning on Non-IID Data With Resource Constraint

IEEE Transactions on Computers, 2022
Federated learning (FL) has been widely recognized as a promising approach by enabling individual end-devices to cooperatively train a global model without exposing their own data. One of the key challenges in FL is the non-independent and identically distributed (Non-IID) data across the clients, which decreases the efficiency of stochastic gradient ...
Jie Zhang 0076, Song Guo 0001, Zhihao Qu, Deze Zeng, Yufeng Zhan, Qifeng Liu, Rajendra Akerkar   +6 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

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
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

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.
openaire   +1 more source