Federated learning enables 6 G communication technology: Requirements, applications, and integrated with intelligence framework

Hasan, Mohammad Kamrul; Habib, A.K.M. Ahasan; Islam, Shayla; Safie, Nurhizam; Ghazal, Taher M.; Khan, Muhammad Attique; Alzahrani, Ahmed Ibrahim; Alalwan, Nasser; Kadry, Seifedine; Masood, Anum

doi:10.1016/j.aej.2024.02.044

Cited by 4 publications

(1 citation statement)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The article focuses on privacy and security issues of federated learning in smart cities. The articles in [41][42][43] focus on reviewing blockchain in federated learning, while the article in [44] focuses on FL-enabled 6G technology, specifying its requirements, applications, and current challenges. In [45], Liu et al focus on the concept of vertical federated learning and its challenges.…”

Section: Related Workmentioning

confidence: 99%

Communication Efficiency and Non-Independent and Identically Distributed Data Challenge in Federated Learning: A Systematic Mapping Study

Alotaibi,

Khan,

Mahmood

2024

Applied Sciences

View full text Add to dashboard Cite

Federated learning has emerged as a promising approach for collaborative model training across distributed devices. Federated learning faces challenges such as Non-Independent and Identically Distributed (non-IID) data and communication challenges. This study aims to provide in-depth knowledge in the federated learning environment by identifying the most used techniques for overcoming non-IID data challenges and techniques that provide communication-efficient solutions in federated learning. The study highlights the most used non-IID data types, learning models, and datasets in federated learning. A systematic mapping study was performed using six digital libraries, and 193 studies were identified and analyzed after the inclusion and exclusion criteria were applied. We identified that enhancing the aggregation method and clustering are the most widely used techniques for non-IID data problems (used in 18% and 16% of the selected studies), and a quantization technique was the most common technique in studies that provide communication-efficient solutions in federated learning (used in 27% and 15% of the selected studies). Additionally, our work shows that label distribution skew is the most used case to simulate a non-IID environment, specifically, the quantity label imbalance. The supervised learning model CNN model is the most commonly used learning model, and the image datasets MNIST and Cifar-10 are the most widely used datasets when evaluating the proposed approaches. Furthermore, we believe the research community needs to consider the client’s limited resources and the importance of their updates when addressing non-IID and communication challenges to prevent the loss of valuable and unique information. The outcome of this systematic study will benefit federated learning users, researchers, and providers.

show abstract

Section: Related Workmentioning

confidence: 99%

Communication Efficiency and Non-Independent and Identically Distributed Data Challenge in Federated Learning: A Systematic Mapping Study

Alotaibi,

Khan,

Mahmood

2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

Design and implementation of privacy-preserving federated learning algorithm for consumer IoT

Zhao,

Ji,

Shi

et al. 2024

Alexandria Engineering Journal

View full text Add to dashboard Cite

Enhanced feature selection and ensemble learning for cardiovascular disease prediction: hybrid GOL2-2 T and adaptive boosted decision fusion with babysitting refinement

Praveen,

Hasan,

Abdullah

et al. 2024

Front. Med.

View full text Add to dashboard Cite

IntroductionGlobal Cardiovascular disease (CVD) is still one of the leading causes of death and requires the enhancement of diagnostic methods for the effective detection of early signs and prediction of the disease outcomes. The current diagnostic tools are cumbersome and imprecise especially with complex diseases, thus emphasizing the incorporation of new machine learning applications in differential diagnosis.MethodsThis paper presents a new machine learning approach that uses MICE for mitigating missing data, the IQR for handling outliers and SMOTE to address first imbalance distance. Additionally, to select optimal features, we introduce the Hybrid 2-Tier Grasshopper Optimization with L2 regularization methodology which we call GOL2-2T. One of the promising methods to improve the predictive modelling is an Adaboost decision fusion (ABDF) ensemble learning algorithm with babysitting technique implemented for the hyperparameters tuning. The accuracy, recall, and AUC score will be considered as the measures for assessing the model.ResultsOn the results, our heart disease prediction model yielded an accuracy of 83.0%, and a balanced F1 score of 84.0%. The integration of SMOTE, IQR outlier detection, MICE, and GOL2-2T feature selection enhances robustness while improving the predictive performance. ABDF removed the impurities in the model and elaborated its effectiveness, which proved to be high on predicting the heart disease.DiscussionThese findings demonstrate the effectiveness of additional machine learning methodologies in medical diagnostics, including early recognition improvements and trustworthy tools for clinicians. But yes, the model’s use and extent of work depends on the dataset used for it really. Further work is needed to replicate the model across different datasets and samples: as for most models, it will be important to see if the results are generalizable to populations that are not representative of the patient population that was used for the current study.

show abstract

Federated learning enables 6 G communication technology: Requirements, applications, and integrated with intelligence framework

Cited by 4 publications

References 30 publications

Communication Efficiency and Non-Independent and Identically Distributed Data Challenge in Federated Learning: A Systematic Mapping Study

Communication Efficiency and Non-Independent and Identically Distributed Data Challenge in Federated Learning: A Systematic Mapping Study

Design and implementation of privacy-preserving federated learning algorithm for consumer IoT

Enhanced feature selection and ensemble learning for cardiovascular disease prediction: hybrid GOL2-2 T and adaptive boosted decision fusion with babysitting refinement

Contact Info

Product

Resources

About

Federated learning enables 6 G communication technology: Requirements, applications, and integrated with intelligence framework

Cited by 4 publications

References 30 publications

Communication Efficiency and Non-Independent and Identically Distributed Data Challenge in Federated Learning: A Systematic Mapping Study

Communication Efficiency and Non-Independent and Identically Distributed Data Challenge in Federated Learning: A Systematic Mapping Study

Design and implementation of privacy-preserving federated learning algorithm for consumer IoT

Enhanced feature selection and ensemble learning for cardiovascular disease prediction: hybrid GOL2-2 T and adaptive boosted decision fusion with babysitting refinement

Contact Info

Product

Resources

About

Federated learning enables 6 G communication technology: Requirements, applications, and integrated with intelligence framework