A Review of Solving Non-IID Data in Federated Learning: Current Status and Future Directions-Reference-Cited by-同舟云学术

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A Review of Solving Non-IID Data in Federated Learning: Current Status and Future Directions

Published:2024 Issue: Volume: Page:58-72
ISSN:1865-0929
Container-title:Communications in Computer and Information Science
language:en
Short-container-title:

Author:

Lu Wenhai,Cheng Jieren,Li Xiulai,He Ji

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-97-1277-9_5

Reference57 articles.

1. Abouelnaga, Y., Ali, O.S., Rady, H., Moustafa, M.: CIFAR-10: KNN-based ensemble of classifiers. In: 2016 International Conference on Computational Science and Computational Intelligence (CSCI), pp. 1192–1195. IEEE (2016)

2. Antunes, R.S., André da Costa, C., Küderle, A., Yari, I.A., Eskofier, B.: Federated learning for healthcare: systematic review and architecture proposal. ACM Trans. Intell. Syst. Technol. (TIST) 13(4), 1–23 (2022)

3. Banabilah, S., Aloqaily, M., Alsayed, E., Malik, N., Jararweh, Y.: Federated learning review: fundamentals, enabling technologies, and future applications. Inf. Process. Manage. 59(6), 103061 (2022)

4. Cheng, J., Luo, P., Xiong, N., Wu, J.: AAFL: asynchronous-adaptive federated learning in edge-based wireless communication systems for countering communicable infectious diseasess. IEEE J. Sel. Areas Commun. 40(11), 3172–3190 (2022)

5. Chiaro, D., Prezioso, E., Ianni, M., Giampaolo, F.: FL-enhance: a federated learning framework for balancing Non-IID data with augmented and shared compressed samples. Inf. Fusion 98, 101836 (2023)

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1. Evaluation of Data Heterogeneity in FL Environment;2024 XXVII International Conference on Soft Computing and Measurements (SCM);2024-05-22

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