Push Sum

Push-sum is a distributed computing algorithm designed to achieve average consensus among multiple agents in a network, enabling collaborative computation and optimization. Current research focuses on enhancing its robustness against network failures (like packet loss) and adversarial attacks (e.g., Byzantine failures), often employing hierarchical architectures and incorporating techniques like dual averaging and variance reduction to improve convergence speed and efficiency. These advancements are significant for distributed machine learning, particularly in scenarios with unreliable communication or malicious actors, enabling efficient and secure collaborative optimization in diverse applications.