Bitwidth Heterogeneous Federated Learning with Progressive Weight
Dequantization

Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantization

23 February 2022

Papers citing "Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantization"

9 / 9 papers shown

Title
Photon: Federated LLM Pre-Training Lorenzo Sani Alex Iacob Zeyu Cao Royson Lee Bill Marino ... Dongqi Cai Zexi Li Wanru Zhao Xinchi Qiu Nicholas D. Lane AI4CE 36 7 0 05 Nov 2024
Recurrent Early Exits for Federated Learning with Heterogeneous Clients Royson Lee Javier Fernandez-Marques S. Hu Da Li Stefanos Laskaridis L. Dudziak Timothy M. Hospedales Ferenc Huszár Nicholas D. Lane 36 3 0 23 May 2024
Fed-QSSL: A Framework for Personalized Federated Learning under Bitwidth and Data Heterogeneity Yiyue Chen H. Vikalo C. Wang FedML 39 5 0 20 Dec 2023
Mixed-Precision Quantization for Federated Learning on Resource-Constrained Heterogeneous Devices Huancheng Chen H. Vikalo FedML MQ 16 7 0 29 Nov 2023
Heterogeneous Federated Learning: State-of-the-art and Research Challenges Mang Ye Xiuwen Fang Bo Du PongChi Yuen Dacheng Tao FedML AAML 39 244 0 20 Jul 2023
Conformal Prediction for Federated Uncertainty Quantification Under Label Shift Vincent Plassier Mehdi Makni Aleksandr Rubashevskii Eric Moulines Maxim Panov FedML 21 15 0 08 Jun 2023
Quantization Aware Attack: Enhancing Transferable Adversarial Attacks by Model Quantization Yulong Yang Chenhao Lin Qian Li Zhengyu Zhao Haoran Fan Dawei Zhou Nannan Wang Tongliang Liu Chao Shen AAML MQ 32 12 0 10 May 2023
Closing the Dequantization Gap: PixelCNN as a Single-Layer Flow Didrik Nielsen Ole Winther MQ 201 13 0 06 Feb 2020
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization Amirhossein Reisizadeh Aryan Mokhtari Hamed Hassani Ali Jadbabaie Ramtin Pedarsani FedML 174 760 0 28 Sep 2019