Partial Gathering

Partial gathering, a subfield of distributed systems and robotics, focuses on algorithms enabling groups of agents (robots, data points, etc.) to converge partially or fully to a common location or state, often under constraints like limited visibility or communication. Current research explores algorithms that preserve symmetry during gathering, develop efficient communication strategies for large-scale distributed learning (e.g., using gather-and-distribute mechanisms and sparse communication), and address challenges in dynamic environments or with noisy data. These advancements have implications for various applications, including swarm robotics, drug discovery (predicting gene-disease links), and efficient object detection in computer vision, improving performance and scalability in these fields.