Federated Learning (FL) has recently attracted increasing attention due to its ability to enable knowledge sharing while preserving user data, protecting privacy, improving learning efficiency, and reducing communication overhead. Unlike Centralized Federated Learning (CFL), Decentralized Federated Learning (DFL) is based on a decentralized network architecture that eliminates the need for a central server. DFL enables direct communication among clients, thereby significantly reducing communication resource consumption.
| Mualliflar | Kuzibayev, Khudayshukur, Beymamatov, Khudoybergan |
|---|---|
| Jurnal | Techscience.uz - техника фанлари долзарб масалалри |
| Nashr sanasi | 2026-01-31 |
| Jild | 4 |
| Son | 1 |
| Betlar | 19-28 |
| Til | O‘zbek |
| DOI | 10.47390/ts-v4i1y2026n03 |
DOI: 10.47390/ts-v4i1y2026n03 · Maqolaning asl sahifasi
federated learning, decentralized learning, network, privacy preservation, Internet of Things (IoT)., Federativ o ‘qitish, markazlashtirilmagan o‘rganish, tarmoq, maxfiylikni saqlash, buyumlar interneti (Internet of Things, IoT).
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