Collaborative Pattern

Collaborative pattern research explores how multiple entities, whether individuals, devices, or AI agents, can pool resources and expertise to achieve a shared goal, often surpassing individual capabilities. Current research focuses on optimizing collaboration strategies across diverse applications, including federated learning, multi-agent systems, and human-AI interaction, employing techniques like weighted gradient averaging, consensus-based methods, and clustering algorithms to improve efficiency and mitigate negative interactions. This work is significant for advancing machine learning, improving the performance of distributed systems, and enhancing human-computer collaboration in various fields, from scientific research to autonomous driving.