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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
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Federated Learning With Non-IID Data: A Survey
IEEE Internet of Things JournalYueyue Dai, Yan Zhang, Heng Pan
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Federated Learning With Non-IID Data in Wireless Networks
IEEE Transactions on Wireless Communications, 2022Federated 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 ...
Tony Q. S. Quek +2 more
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FedProc: Prototypical contrastive federated learning on non-IID data
Future Generation Computer Systems, 2023 Federated learning allows multiple clients to collaborate to train high-performance deep learning models while keeping the training data locally. However, when the local data of all clients are not independent and identically distributed (i.e., non-IID), it is challenging to implement this form of efficient collaborative learning.
Ke Cheng, Yulong Shen, Xutong Mu
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Low precision decentralized distributed training over IID and non-IID data [PDF]
Neural Networks, 2022 Decentralized distributed learning is the key to enabling large-scale machine learning (training) on edge devices utilizing private user-generated local data, without relying on the cloud.
Sangamesh D Kodge +2 more
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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
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Non-IIDness Learning in Behavioral and Social Data
The Computer Journal, 2013Most 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), 2022Yuanqin Cai, Tongquan Wei
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Kernel Measures of Independence for non-iid Data.
2009Many 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. +3 more
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FedEL: Federated ensemble learning for non-iid data
Expert Systems with ApplicationsFederated 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 +7 more
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