Bidirectional Compression

Bidirectional compression aims to reduce communication overhead in distributed machine learning by compressing data transmitted in both directions—between worker nodes and a central server—during model training. Current research focuses on developing novel algorithms, such as those incorporating error compensation and adaptive compression levels, to improve convergence speed and communication efficiency while using various compression techniques (e.g., sparsification, quantization). These advancements are significant because they accelerate training of large models, particularly in resource-constrained environments like federated learning, and enable the efficient use of distributed computing resources for complex machine learning tasks.