MARKAZLASHGAN FEDERATIV O‘QITISH MUAMMOLARI

Mualliflar

  • Xudayshukur Quzibayev
  • Xudoybergan Beymamatov

Kalit so'zlar

https://doi.org/10.47390/ts-v4i1y2026N03

Kalit so'zlar

Federativ o ‘qitish, markazlashtirilmagan o‘rganish, tarmoq, maxfiylikni saqlash, buyumlar interneti (Internet of Things, IoT).

Annotasiya

Federativ o‘rganish (Federated Learning, FL) foydalanuvchi ma’lumotlarini saqlagan holda bilim almashish imkoniyati, maxfiylikni himoyalash, o‘rganish samaradorligini oshirish hamda aloqa (kommunikatsiya) yuklamasini kamaytirish xususiyatlari tufayli tobora ko‘proq e’tiborni tortmoqda. Markazlashtirilmagan federativ o‘rganish (Decentralized Federated Learning, DFL) markaziy federativ o‘rganishdan (Centralized Federated Learning, CFL) farqli ravishda, markaziy serverga bo‘lgan ehtiyojni bartaraf etuvchi markazlashtirilmagan tarmoq arxitekturasiga asoslanadi. DFL mijozlar (klientlar) o‘rtasida bevosita aloqa o‘rnatishni ta’minlab, kommunikatsiya resurslarini sezilarli darajada tejash imkonini beradi.

Manbalar

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6. A. R. Elkordy, Y. H. Ezzeldin, S. Han, S. Sharma, C. He, S. Mehrotra, S. Avestimehr et al., “Federated analytics: A survey,” APSIPA Transactions on Signal and Information Processing, vol. 12, no. 1, 2023.

7. D. Chen, D. Wang, Y. Zhu, and Z. Han, “Digital twin for federated analytics using a bayesian approach,” IEEE Internet of Things Journal,vol. 8, no. 22, pp. 16301–16312, 2021.

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9. Z. Wang, Y. Zhu, D. Wang, and Z. Han, “FedFPM: A unified federated analytics framework for collaborative frequent pattern mining,” in IEEE INFOCOM 2022-IEEE Conference on Computer Communications. IEEE, 2022, pp. 61–70.

10. D. Froelicher, J. R. Troncoso-Pastoriza, J. L. Raisaro, M. A. Cuendet, J. S. Sousa, H. Cho, B. Berger, J. Fellay, and J.-P. Hubaux, “Truly privacy-preserving federated analytics for precision medicine with multiparty homomorphic encryption,” Nature communications, vol. 12, no. 1, p. 5910, 2021.

11. Z. Wang, R. Gupta, K. Han, H. Wang, A. Ganlath, N. Ammar, and P. Tiwari, “Mobility digital twin: Concept, architecture, case study, and future challenges,” IEEE Internet Things J., Mar. 2022.

12. L. U. Khan, W. Saad, Z. Han, E. Hossain, and C. S. Hong, “Federated learning for internet of things: Recent advances, taxonomy, and open challenges,” IEEE Communications Surveys & Tutorials, vol. 23, no. 3, pp. 1759–1799, 2021.

13. Y. Li, B. Dang, W. Li, and Y. Zhang, “Glh-water: A large-scale dataset for global surface water detection in large-size very-high-resolution satellite imagery,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, no. 20, 2024, pp. 22213–22221.

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Yuborilgan

2026-01-30

Nashr qilingan

2026-01-31

Qanday ko'rsatish

Quzibayev, X., & Beymamatov, X. (2026). MARKAZLASHGAN FEDERATIV O‘QITISH MUAMMOLARI. Techscience Uz - Topical Issues of Technical Sciences, 4(1), 19–28. https://doi.org/10.47390/ts-v4i1y2026N03

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