Stochastic Quantization

Stochastic quantization (SQ) is a technique used to reduce the computational and communication costs associated with large-scale machine learning models by representing model parameters with fewer bits. Current research focuses on applying SQ to improve the efficiency and robustness of various algorithms, including federated learning, clustering, and deep neural network training, often incorporating adaptive quantization schemes and differentiable quantizers to mitigate accuracy loss. This work is significant because it addresses critical bottlenecks in deploying large models, enabling faster training, reduced communication overhead, and enhanced privacy in distributed settings.