IEEE

Lightweight Adaptive Quantization Algorithms for Federated Learning with Heterogeneous Clients

Abstract: Quantization is a common method to improve communication efficiency in federated learning (FL) by compressing the gradients that clients upload. Currently, most application scenarios involve ...
IEEE

A Novel Privacy Enhancement Scheme with Dynamic Quantization for Federated Learning

Abstract: Federated learning (FL) has been widely regarded as a promising paradigm for privacy preservation of raw data in machine learning. Although data privacy in FL is locally protected to some ...
GitHub

[ICCV 2025] D³QE: Learning Discrete Distribution Discrepancy-aware Quantization Error for Autoregressive-Generated Image Detection

Integrates dynamic codebook frequency statistics into a transformer attention module. Fuses semantic image features with latent representations of quantization ...
GitHub

Robust Residual Finite Scalar Quantization for Neural Compression

This repository contains the official implementation of Robust Residual Finite Scalar Quantization (RFSQ), a novel quantization framework that addresses the residual magnitude decay problem in naive ...