Compression Scheme

Compression schemes aim to reduce the size of data, particularly in resource-constrained environments like distributed machine learning and data transmission. Current research focuses on developing efficient compression algorithms for various data types, including neural network weights, gradients, and high-dimensional data like light fields, often leveraging techniques like quantization, pruning, tensor networks, and compressed sensing. These advancements are crucial for improving the efficiency and scalability of machine learning models and enabling the processing and transmission of large datasets in applications ranging from federated learning to biomedical data analysis.