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Detecting Outliers in Non-IID Data: A Systematic Literature Review
IEEE Access, 2023 Outlier detection (outlier and anomaly are used interchangeably in this review) in non-independent and identically distributed (non-IID) data refers to identifying unusual or unexpected observations in datasets that do not follow an independent and ...
Shafaq Siddiqi +3 more
doaj +4 more sources
Federated Learning with Non-IID Data [PDF]
CoRR, 2022 Federated learning enables resource-constrained edge compute devices, such as mobile phones and IoT devices, to learn a shared model for prediction, while keeping the training data local.
Suda, Naveen +5 more
core +4 more sources
Homophily outlier detection in non-IID categorical data [PDF]
Data Mining and Knowledge Discovery, 2021 Most of existing outlier detection methods assume that the outlier factors (i.e., outlierness scoring measures) of data entities (e.g., feature values and data objects) are Independent and Identically Distributed (IID).
CAO, Longbing +5 more
core +3 more sources
Shallow and Deep Non-IID Learning on Complex Data
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023 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).
Yu, PS, Zhao, Z, Cao, LL
core +3 more sources
Client Selection for Federated Learning With Non-IID Data in Mobile Edge Computing
IEEE Access, 2021 Federated Learning (FL) has recently attracted considerable attention in internet of things, due to its capability of enabling mobile clients to collaboratively learn a global prediction model without sharing their privacy-sensitive data to the server ...
Weiwei Wu, Weiwei Wu, Xiumin Wang
exaly +3 more sources
Federated Learning With Taskonomy for Non-IID Data
IEEE Transactions on Neural Networks and Learning Systems, 2022 Classical federated learning approaches incur significant performance degradation in the presence of non-independent and identically distributed (non-IID) client data. A possible direction to address this issue is forming clusters of clients with roughly
Abdizadeh, Mohammad (author) +2 more
core +7 more sources
Secure and decentralized federated learning framework with non-IID data based on blockchain [PDF]
HeliyonFederated learning enables the collaborative training of machine learning models across multiple organizations, eliminating the need for sharing sensitive data.
Feng Zhang +3 more
doaj +2 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.
Wang, Yansheng +6 more
core +2 more sources
Peer-to-Peer Learning + Consensus with Non-IID Data
2023 57th Asilomar Conference on Signals, Systems, and Computers, 2023 Peer-to-peer deep learning algorithms are enabling distributed edge devices to collaboratively train deep neural networks without exchanging raw training data or relying on a central server.
Pranav, Srinivasa, Moura, José M. F.
core +3 more sources
Federated XGBoost on Sample-Wise Non-IID Data
CoRR, 2022 Federated Learning (FL) is a paradigm for jointly training machine learning algorithms in a decentralized manner which allows for parties to communicate with an aggregator to create and train a model, without exposing the underlying raw data distribution
Jones, Katelinh +3 more
core +3 more sources