Learning and Optimization over Robust Networked Systems

Research Summary: 1 Introduction Networked systems are ubiquitous in our daily lives, playing a critical role across a wide range of scientific fields, including communication, machine learning, optimization, control, biology, economics, and social sciences. In machine learning and optimization, distributed learning and distributed optimization exploit the structure of such networked systems, which enables training models across multiple users [1] and solving complex problems by partitioning tasks among interconnected agents [2]. This significantly enhances both scalability and efficiency of the learning/optimization task. In control systems, for example, networked architectures synchronize the operations of different parts of the system like sensors, controllers etc. across distributed environments, ensuring the overall system's stability and performance [3]. In economics, networked systems were utilized to capture the interdependencies between different part of the economic system, allowing further insights of market dynamics [4]. In the social sciences, specifically social networks, study the propagation of idea, behaviors and opinions, offering a deeper understanding of societal structures and its dynamics [5].