Guest Editorial: Special Section on the Latest Developments in Federated Learning for the Management of Networked Systems and Resources

被引：0

作者：

Mourad, Azzam ^{[1
]}

Otrok, Hadi ^{[2
]}

Damiani, Ernesto ^{[3
]}

Debbah, Merouane ^{[4
]}

Guizani, Nadra ^{[5
]}

Wang, Shiqiang ^{[6
]}

Han, Guangjie ^{[7
]}

Mizouni, Rabeb ^{[2
]}

Bentahar, Jamal ^{[8
]}

Talhi, Chamseddine ^{[9
]}

机构：

[1] Lebanese Amer Univ, Comp Sci & Math Dept, Beirut 1102 2801, Lebanon

[2] Khalifa Univ, Dept Elect Engn & Comp Sci, Abu Dhabi, U Arab Emirates

[3] Univ Milan, Dept Comp Sci, Milan 20122, Italy

[4] Technol Innovat Inst, Abu Dhabi, U Arab Emirates

[5] Univ Texas Arlington, Dept Comp Sci & Engn, Arlington, TX 20122 USA

[6] IBM TJ Watson Res Ctr, Yorktown Hts, NY 10598 USA

[7] Hohai Univ Changzhou, Dept Internet Things Engn, Changzhou 213022, Peoples R China

[8] Concordia Inst Informat Syst Engn CIISE Concordia, Montreal, PQ H3G 1M8, Canada

[9] Ecole Technol Suprieure & IT, Dept Software Engn, Montreal, PQ H3C 1K3, Canada

来源：

IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT | 2023年 / 20卷 / 02期

关键词：

Special issues and sections; Privacy; Deep learning; Federated learning; Telecommunication network management; Network resource management;

D O I：

10.1109/TNSM.2023.3282130

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Driven by privacy concerns and the promise of Deep Learning, researchers have devoted significant effort to exploring the applicability of Machine Learning (ML). In the domains of communication, network, and service management, ML-based decision-making solutions are eagerly sought to replace traditional model-driven approaches, addressing the growing complexity and heterogeneity of modern systems. In this context, Federated Learning (FL) has gained increasing interest as a decentralized approach that overcomes the limitations of centralized systems for data analysis.

引用

页码：1441 / 1445

页数：5