Top K Sparsification

Top-K sparsification is a technique used to reduce communication overhead in distributed machine learning by transmitting only the K largest (in magnitude) elements of gradients or activations. Current research focuses on improving the efficiency and accuracy of this method across various applications, including large language models, federated learning, and model-parallel training, often employing techniques like Bayesian inference or randomized sparsification to enhance performance. This approach is crucial for scaling up deep learning to larger models and datasets, addressing communication bottlenecks in distributed settings and improving the energy efficiency of training and inference.