Gradient Compression

Gradient compression aims to reduce the communication overhead in distributed machine learning by transmitting smaller representations of model updates (gradients). Current research focuses on developing novel compression techniques, including quantization, sparsification, low-rank approximation, and the use of large language models as gradient priors, often incorporating error feedback mechanisms to mitigate information loss. These advancements are crucial for scaling up training of large models like LLMs and for enabling efficient federated learning in resource-constrained environments, ultimately accelerating training speed and reducing energy consumption.