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.

Today, machine learning algorithms are often distributed across multiple machines to leverage more computing power and more data. However, the use of a distributed framework entails a variety of security threats. In particular, some of the machines may misbehave and jeopardize the learning procedure. This could, for example, result from hardware and software bugs, data poisoning or a malicious player controlling a subset of the machines. This book explains in simple terms what it means for a distributed machine learning scheme to be robust to these threats, and how to build provably robust machine learning algorithms.

In addition to introducing the problem of robustness in modern machine learning algorithms, the book will equip readers with essential skills for designing distributed learning algorithms with enhanced robustness. Moreover, the book provides a foundation for future research in this area.

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.

Today, machine learning algorithms are often distributed across multiple machines to leverage more computing power and more data. However, the use of a distributed framework entails a variety of security threats. In particular, some of the machines may misbehave and jeopardize the learning procedure. This could, for example, result from hardware and software bugs, data poisoning or a malicious player controlling a subset of the machines. This book explains in simple terms what it means for a distributed machine learning scheme to be robust to these threats, and how to build provably robust machine learning algorithms.

In addition to introducing the problem of robustness in modern machine learning algorithms, the book will equip readers with essential skills for designing distributed learning algorithms with enhanced robustness. Moreover, the book provides a foundation for future research in this area.