SSTQ enables federated learning systems to compress communications to logarithmic bit-widths while guaranteeing local differential privacy—a significant improvement over prior methods that suffered from dimension-dependent variance.
This paper introduces SSTQ, a privacy-preserving vector quantization method for distributed learning that compresses high-dimensional data into fewer bits while protecting individual privacy.