Linear Speedup

Linear speedup in computation aims to achieve a proportional increase in processing speed with the addition of computational resources (e.g., processors, agents). Current research focuses on achieving this in various machine learning contexts, including federated learning, reinforcement learning, and evolutionary algorithms, often employing techniques like asynchronous updates, efficient gradient aggregation, and adaptive learning rates within diverse model architectures such as neural networks and support vector machines. Demonstrating provable linear speedups, particularly in non-convex and non-IID data settings, is a major focus, with implications for scaling up complex algorithms and reducing the computational cost of training large models. This research directly impacts the efficiency and scalability of machine learning applications, enabling faster training and deployment of sophisticated models.