Asynchronous Update

Asynchronous updates represent a paradigm shift in distributed computation, aiming to improve efficiency and robustness by allowing individual components to update independently, rather than synchronously. Current research focuses on applying this approach to diverse fields, including model predictive control for robotics, cellular automata simulations, and decentralized machine learning, often employing adaptive strategies to mitigate the challenges of inconsistent update times and data staleness. This approach holds significant promise for enhancing the scalability and performance of complex systems, particularly in resource-constrained or unpredictable environments like wireless networks and large-scale machine learning training.