An Efficient Quantized Message Passing Receiver Design for SCMA Systems by Hao Cheng, Min Zhang, Ruoyu Su

“…In this sense, we propose a new quasi-uniform quantization scheme that can efficiently handle the dynamic range in the exchange of messages. …”
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Accelerating Energy-Efficient Federated Learning in Cell-Free Networks With Adaptive Quantization by Afsaneh Mahmoudi, Ming Xiao, Emil Bjornson

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Data-oriented optimized nonuniform quantization for CR-enhanced communication efficiency in federated learning by Shuai Luo, Qiming Wan, Hongrui Wang, Tianchun Xiang, Yang Wang, Xin He, Wei Zhang

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BiPruneFL: Computation and Communication Efficient Federated Learning With Binary Quantization and Pruning by Sangmin Lee, Hyeryung Jang

“…Existing solutions typically apply compression techniques such as quantization or pruning but only to a limited extent, constrained by the trade-off between model accuracy and compression efficiency. …”
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Enhanced Privacy and Communication Efficiency in Non-IID Federated Learning With Adaptive Quantization and Differential Privacy by Emre Ardic, Yakup Genc

“…To address both privacy and communication efficiency, we combine differential privacy (DP) and adaptive quantization methods. …”
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Efficient Spectral Compression of Wavelength-Shifting Soliton and Its Application in Integratable All-Optical Quantization by Chao Mei, Jinhui Yuan, Feng Li, Binbin Yan, Xinzhu Sang, Qiang Wu, Xian Zhou, Kuiru Wang, Chongxiu Yu, Gerald Farrell

“…In this paper, we numerically demonstrate efficient spectral compression (SPC) of wavelength-shifting soliton in a chalcogenide strip waveguide. …”
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Ultimate Compression: Joint Method of Quantization and Tensor Decomposition for Compact Models on the Edge by Mohammed Alnemari, Nader Bagherzadeh

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Learning from low precision samples by Ji In Choi, Madeleine Georges, Jung Ah Shin, Olivia Wang, Tiffany Zhu, Tapan Shah

Subjects: “…efficient machine learning…”
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PP-QADMM: A Dual-Driven Perturbation and Quantized ADMM for Privacy Preserving and Communication-Efficient Federated Learning by Anis Elgabli

Subjects: “…Communication-efficient federated learning…”
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Rate distortion optimization for adaptive gradient quantization in federated learning by Guojun Chen, Kaixuan Xie, Wenqiang Luo, Yinfei Xu, Lun Xin, Tiecheng Song, Jing Hu

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AI for Sustainable Recycling: Efficient Model Optimization for Waste Classification Systems by Oriol Chacón-Albero, Mario Campos-Mocholí, Cédric Marco-Detchart, Vicente Julian, Jaime Andrés Rincon, Vicent Botti

“…In this work, we extend our previous research by improving both dataset diversity and model efficiency. We introduce an expanded dataset that includes an organic waste class and more heterogeneous images, and evaluate a range of quantized CNN models to reduce inference time and resource usage. …”
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Efficient Detection of Microplastics on Edge Devices With Tailored Compiler for TinyML Applications by Alessandro Cerioli, Lorenzo Petrosino, Daniele Sasso, Clement Laroche, Tobias Piechowiak, Luca Pezzarossa, Mario Merone, Luca Vollero, Anna Sabatini

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HLQ: Hardware-Friendly Logarithmic Quantization Aware Training for Power-Efficient Low-Precision CNN Models by Dahun Choi, Juntae Park, Hyun Kim

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Efficient Deep Learning Model Compression for Sensor-Based Vision Systems via Outlier-Aware Quantization by Joonhyuk Yoo, Guenwoo Ban

“…By analyzing previous outlier-handling techniques using structural similarity (SSIM) measurement results, we demonstrated that OAQ significantly reduced the negative impact of outliers while maintaining computational efficiency. Notably, OAQ was orthogonal to existing quantization schemes, making it compatible with various quantization methods without additional computational overhead. …”
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Addressing Activation Outliers in LLMs: A Systematic Review of Post-Training Quantization Techniques by Patrik Czako, Gabor Kertesz, Sandor Szenasi

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Efficient secure federated learning aggregation framework based on homomorphic encryption by Shengxing YU, Zhong CHEN

“…In order to solve the problems of data security and communication overhead in federated learning, an efficient and secure federated aggregation framework based on homomorphic encryption was proposed.In the process of federated learning, the privacy and security issues of user data need to be solved urgently.However, the computational cost and communication overhead caused by the encryption scheme would affect the training efficiency.Firstly, in the case of protecting data security and ensuring training efficiency, the Top-K gradient selection method was used to screen model gradients, reducing the number of gradients that need to be uploaded.A candidate quantization protocol suitable for multi-edge terminals and a secure candidate index merging algorithm were proposed to further reduce communication overhead and accelerate homomorphic encryption calculations.Secondly, since model parameters of each layer of neural networks had characteristics of the Gaussian distribution, the selected model gradients were clipped and quantized, and the gradient unsigned quantization protocol was adopted to speed up the homomorphic encryption calculation.Finally, the experimental results show that in the federated learning scenario, the proposed framework can protect data privacy, and has high accuracy and efficient performance.…”
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Enhancing molecular property prediction with quantized GNN models by Areen Rasool, Jamshaid Ul Rahman, Rongin Uwitije

“…This paper presents a systematic approach to molecular networks by integrating GNN models with the DoReFa-Net quantization algorithm. The proposed method aims to enhance computational efficiency while maintaining predictive performance, enabling lightweight yet effective models suitable for molecular task. …”
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Large language models for PHM: a review of optimization techniques and applications by Tingyi Yu, Junya Tang, Qingyun Yu, Li Li, Ying Liu, Raul Poler

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Toward Generating Quality Test Questions and Answers Using Quantized Low-Rank Adapters in LLMs by Jebum Choi, Seongjun Hong, Seoyoon Hong, Jiyeon Park, Eun-Sung Jung

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SparseBatch: Communication-efficient Federated Learning with Partially Homomorphic Encryption by Chong Wang, Jing Wang, Zheng Lou, Linghai Kong, WeiSong Tao, Yun Wang

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