Dynamic Consensus

Dynamic consensus focuses on developing algorithms enabling distributed networks of agents to agree on a common value, even in the presence of noisy data, adversarial agents, or time-varying signals. Current research emphasizes robust algorithms, such as median-based approaches and those leveraging high-order sliding modes, to achieve exact consensus even with limited communication or non-uniform agent behavior. These advancements are crucial for applications ranging from distributed optimization and machine learning to multi-robot coordination and resilient information dissemination, improving efficiency and scalability in complex systems.