Enhancing Decentralized Federated Learning for Non-IID Data on Heterogeneous Devices

Chen, Min; Xu, Yang; Xu, Hongli; Huang, Liusheng

doi:10.1109/icde55515.2023.00177

Cited by 5 publications

(1 citation statement)

References 36 publications

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“…Because of disabilities, organizations and individuals encounter considerable difficulties. Via the use of the AI-based recommendation system [23] and federated learning, this paper focuses on providing reasonable output in disability management. Therefore, many methods introduced in this domain offer the best effectiveness in disability management; here, the existing methods are not able to generate a model for the improvement of disability management and have challenges such as computational cost, required amount of data, weight optimizations, etc.…”

Section: Research Gapmentioning

confidence: 99%

Advancing Disability Management in Information Systems: A Novel Approach through Bidirectional Federated Learning-Based Gradient Optimization

Khan,

Alojail,

Al Moteri

2023

Mathematics

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Disability management in information systems refers to the process of ensuring that digital technologies and applications are designed to be accessible and usable by individuals with disabilities. Traditional methods face several challenges such as privacy concerns, high cost, and accessibility issues. To overcome these issues, this paper proposed a novel method named bidirectional federated learning-based Gradient Optimization (BFL-GO) for disability management in information systems. In this study, bidirectional long short-term memory (Bi-LSTM) was utilized to capture sequential disability data, and federated learning was employed to enable training in the BFL-GO method. Also, gradient-based optimization was used to adjust the proposed BFL-GO method’s parameters during the process of hyperparameter tuning. In this work, the experiments were conducted on the Disability Statistics United States 2018 dataset. The performance evaluation of the BFL-GO method involves analyzing its effectiveness based on evaluation metrics, namely, specificity, F1-score, recall, precision, AUC-ROC, computational time, and accuracy and comparing its performance against existing methods to assess its effectiveness. The experimental results illustrate the effectiveness of the BFL-GO method for disability management in information systems.

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Section: Research Gapmentioning

confidence: 99%

Advancing Disability Management in Information Systems: A Novel Approach through Bidirectional Federated Learning-Based Gradient Optimization

Khan,

Alojail,

Al Moteri

2023

Mathematics

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Leveraging local data sampling strategies to improve federated learning

Düsing,

Cimiano,

Paaßen

2024

Int J Data Sci Anal

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Federated learning (FL) facilitates shared training of machine learning models while maintaining data privacy. Unfortunately, it suffers from data imbalance among participating clients, causing the performance of the shared model to drop. To diminish the negative effects of unfavourable data-specific properties, both algorithm- and data-based approaches seek to make FL more resilient against them. In this regard, data-based approaches prove to be more versatile and require less domain knowledge to be applied efficiently. Hence, they seem particularly suitable for widespread application in various FL environments. Although data-based approaches such as local data sampling have been applied to FL in the past, previous research did not provide a systematic analysis of the potential and limitations of individual data sampling strategies to improve FL. To this end, we (1) identify relevant local data sampling strategies applicable to FL systems, (2) identify data-specific properties that negatively affect FL system performance, and (3) provide a benchmark of local data sampling strategies regarding their effect on model performance, convergence, and training time in synthetic, real-world, and large-scale FL environments. Moreover, we propose and rigorously test a novel method for data sampling in FL that locally optimizes the choice of sampling strategy prior to FL participation. Our results show that FL can greatly benefit from applying local data sampling in terms of performance and convergence rate, especially when data imbalance is high or the number of clients and samples is low. Furthermore, our proposed sampling strategy offers the best trade-off between model performance and training time.

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Feed: Towards Personalization-Effective Federated Learning

Qiao,

Zhao,

et al. 2024

2024 IEEE 40th International Conference on Data Engineering (ICDE)

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Enhancing Decentralized Federated Learning for Non-IID Data on Heterogeneous Devices

Cited by 5 publications

References 36 publications

Advancing Disability Management in Information Systems: A Novel Approach through Bidirectional Federated Learning-Based Gradient Optimization

Advancing Disability Management in Information Systems: A Novel Approach through Bidirectional Federated Learning-Based Gradient Optimization

Leveraging local data sampling strategies to improve federated learning

Feed: Towards Personalization-Effective Federated Learning

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