Straggler Mitigation

Straggler mitigation addresses the performance bottleneck in distributed machine learning caused by slow or failing worker nodes, aiming to improve training efficiency and reduce overall computation time. Current research focuses on techniques like gradient coding, asynchronous algorithms (e.g., asynchronous federated learning), and dynamic resource allocation strategies, often tailored to specific architectures (e.g., parameter servers, hierarchical systems, serverless environments). Effective straggler mitigation is crucial for scaling machine learning to larger datasets and more complex models, impacting both the speed and feasibility of training across diverse applications, from deep learning to federated learning.