Distributed Optimization Under Information Constraints: A Survey

Distributed optimization, as a key technology for collaborative intelligence in multiagent systems, has been widely applied in sensor networks, deep learning, and smart grids. Although numerous effective algorithms have been proposed, classical methods typically rely on idealized assumptions, such as accurate objective information, perfect communication channels, and trustworthy system environments. However, these assumptions are frequently violated in real-world applications. To bridge the gap between theory and practice, distributed optimization under information constraints has emerged as a research focus. This survey provides a systematic overview of recent advances in this field. We categorize information constraints based on their origin into three primary types: i) observational constraints, including stochastic objectives, online optimization, and zeroth-order methods; ii) communication constraints, such as random network topologies, delays, asynchronous updates, and communication-efficient strategies; and iii) system-level constraints, encompassing privacy preservation and Byzantine-resilient optimization. This survey reviews the research progress and challenges associated with each constraint category. Furthermore, we use two representative case studies to analyze the practical application of these algorithms and the origins of information constraints in real-world problems. Finally, we explore promising future research directions.