Momentum Based

Momentum-based optimization methods aim to accelerate the convergence of iterative algorithms by incorporating information from previous iterations, improving efficiency and potentially generalization performance. Current research focuses on enhancing robustness and efficiency in distributed and federated learning settings, addressing challenges like Byzantine failures and mitigating catastrophic forgetting in large language models, often through novel momentum-based algorithms and adaptive learning rate schemes. These advancements have significant implications for training large-scale machine learning models, particularly in resource-constrained environments and applications requiring high accuracy and stability.