Decentralized Algorithm

Decentralized algorithms aim to solve computational problems by distributing tasks among multiple autonomous agents, each operating with limited global information and communicating only with neighbors. Current research focuses on improving the efficiency and robustness of these algorithms across diverse applications, including machine learning (e.g., using adaptive gradient methods and random walks), multi-agent systems (e.g., for planning and coordination), and distributed optimization (e.g., employing gradient tracking and consensus-based approaches). This field is significant due to its potential to enhance scalability, privacy, and resilience in large-scale systems, impacting areas such as data processing, network control, and collaborative AI.