Druck auf Anfrage Neuware - Printed after ordering - This book constitutes the refereed proceedings of the 12th International Conference on Networked Systems, NETYS 2024, held in Rabat, Morocco, during May 29-31, 2024.The 14 full papers and 3 short papers presented were carefully reviewed and selected from 42 submissions.NETYS aims to bring together researchers and engineers from the theory and practice of distributed and networked systems. The scope of the conference covers all aspects related to the design and the development of networked systems, e.g.,cloud systems, formal verification, concurrent and distributed algorithms, data management, data science andparallel/concurrent/distributed programming. Additionally, this year NETYS also featured works on the emerging topic of distributed machine learning, alsocommonly referred to asfederated learning.

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Bibliographic Details

Title: Networked Systems : 12th International Conference, NETYS 2024, Rabat, Morocco, May 29-31, 2024, Proceedings
Publisher: Springer, Springer
Publication Date: 2024
Language: English
ISBN 10: 3031673204
ISBN 13: 9783031673207
Binding: Taschenbuch
Condition: Neu
Dimensions: 235 millimeters width by 155 millimeters height by 16 millimeters depth
Weight: 423 grams

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This book constitutes the refereed proceedings of the 12th International Conference on Networked Systems, NETYS 2024, held in Rabat, Morocco, during May 29-31, 2024.

The 14 full papers and 3 short papers presented were carefully reviewed and selected from 42 submissions. NETYS aims to bring together researchers and engineers from the theory and practice of distributed and networked systems. The scope of the conference covers all aspects related to the design and the development of networked systems, e.g., cloud systems, formal verification, concurrent and distributed algorithms, data management, data science and parallel/concurrent/distributed programming. Additionally, this year NETYS also featured works on the emerging topic of distributed machine learning, also commonly referred to as federated learning.

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