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For the 12th consecutive year, NETYS (The International Conference on Networked Systems) 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 these systems, including, but not restricted to, cloud systems, formal verification, concurrent and distributed algorithms, data management, data science, parallel/concurrent/distributed programming, distributed machine-learning, multi-core architectures, networks, and security.

NETYS will provide a forum to report on best practices, novel algorithms, results, and techniques on networked systems. Original research contributions and experience papers on the principles, design, implementation, modeling, analysis, verification and application of networked systems are solicited.
Topics of interest are broadly divided into three categories: networked systems, distributed computing and distributed machine-learning. 

Topics of interest include (but are not limited to):  

1-Networked systems 

• • Cloud systems and data centers
• • Cyber-physical systems
• • Distributed database, embedded and operating systems
• • Multi-core architectures and multithreaded applications
• • Distributed ledgers and blockchain technologies
• • Internet of Things, 5G, URLLC
• • Mobile, wireless, ad-hoc and sensor networks
• • Social networks
• • Overlay and peer-to-peer infrastructures

2-Distributed Computing

• • Concurrency, synchronization and persistence
• • Distributed and concurrent data structures
• • Languages, verification and formal methods for distributed systems
• • Design and analysis of distributed algorithms
• • Lower bounds and impossibility results for distributed computing
• • Game theory, mechanisms design
• • Fault-tolerance, reliability, self-stabilizing, self-organizing, and autonomic systems
• • Collaborative intelligent systems

3-Distributed Machine-Learning 

• • Collaborative/federated learning
• • Learning on peer-to-peer architecture
• • Privacy preserving distributed learning
• • Byzantine-robustness
• • Communication-efficiency
• • Personalized learning
• • Federated ensembling
• • Model compression