FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale
Neural Networks through Federated Learning

FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale Neural Networks through Federated Learning

10 August 2022

Papers citing "FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale Neural Networks through Federated Learning"

19 / 19 papers shown

Title
Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data Ji Liu Juncheng Jia Hong Zhang Yuhui Yun Leye Wang Yang Zhou H. Dai Dejing Dou FedML 30 6 0 11 Aug 2024
MSfusion: A Dynamic Model Splitting Approach for Resource-Constrained Machines to Collaboratively Train Larger Models Jin Xie Songze Li FedML 44 0 0 04 Jul 2024
Exploring the Practicality of Federated Learning: A Survey Towards the Communication Perspective Khiem H. Le Nhan Luong-Ha Manh Nguyen-Duc Danh Le-Phuoc Cuong D. Do Kok-Seng Wong FedML 37 1 0 30 May 2024
WHALE-FL: Wireless and Heterogeneity Aware Latency Efficient Federated Learning over Mobile Devices via Adaptive Subnetwork Scheduling Huai-an Su Jiaxiang Geng Liang Li Xiaoqi Qin Yanzhao Hou Xin Fu Miao Pan Miao Pan 40 1 0 01 May 2024
Advances and Open Challenges in Federated Learning with Foundation Models Chao Ren Han Yu Hongyi Peng Xiaoli Tang Anran Li ... A. Tan Bo Zhao Xiaoxiao Li Zengxiang Li Qiang Yang FedML AIFin AI4CE 78 7 0 23 Apr 2024
FedPFT: Federated Proxy Fine-Tuning of Foundation Models Zhaopeng Peng Xiaoliang Fan Yufan Chen Zheng Wang Shirui Pan Chenglu Wen Ruisheng Zhang Cheng-i Wang 72 8 0 17 Apr 2024
Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Models Weijiao Zhang Jindong Han Zhao Xu Hang Ni Hao Liu Hui Xiong Hui Xiong AI4CE 79 15 0 30 Jan 2024
A Survey on Efficient Federated Learning Methods for Foundation Model Training Herbert Woisetschläger Alexander Isenko Shiqiang Wang R. Mayer Hans-Arno Jacobsen FedML 65 23 0 09 Jan 2024
ZooPFL: Exploring Black-box Foundation Models for Personalized Federated Learning Wang Lu Hao Yu Jindong Wang Damien Teney Haohan Wang Yiqiang Chen Qiang Yang Xing Xie Xiangyang Ji 70 8 0 08 Oct 2023
Towards a Better Theoretical Understanding of Independent Subnetwork Training Egor Shulgin Peter Richtárik AI4CE 28 6 0 28 Jun 2023
Timely Asynchronous Hierarchical Federated Learning: Age of Convergence Purbesh Mitra Sennur Ulukus FedML 21 0 0 21 Jun 2023
Trustworthy Federated Learning: A Survey A. Tariq M. Serhani F. Sallabi Tariq Qayyum E. Barka K. Shuaib FedML 38 9 0 19 May 2023
FedLP: Layer-wise Pruning Mechanism for Communication-Computation Efficient Federated Learning Zheqi Zhu Yuchen Shi Jia Luo Fei Wang Chenghui Peng Pingyi Fan Khaled B. Letaief FedML 40 20 0 11 Mar 2023
Efficient Training of Large-scale Industrial Fault Diagnostic Models through Federated Opportunistic Block Dropout Yuanyuan Chen Zichen Chen Sheng Guo Yansong Zhao Zelei Liu Pengcheng Wu Che-Sheng Yang Zengxiang Li Han Yu AI4CE 38 9 0 22 Feb 2023
Securing Federated Learning: A Covert Communication-based Approach Yuan-ai Xie Jiawen Kang Dusit Niyato Nguyen Thi Thanh Van Nguyen Cong Luong Zhixin Liu Han Yu FedML 42 25 0 05 Oct 2021
Federated Dropout -- A Simple Approach for Enabling Federated Learning on Resource Constrained Devices Dingzhu Wen Ki-Jun Jeon Kaibin Huang FedML 70 90 0 30 Sep 2021
FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout Samuel Horváth Stefanos Laskaridis Mario Almeida Ilias Leondiadis Stylianos I. Venieris Nicholas D. Lane 189 268 0 26 Feb 2021
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization Amirhossein Reisizadeh Aryan Mokhtari Hamed Hassani Ali Jadbabaie Ramtin Pedarsani FedML 174 763 0 28 Sep 2019
Densely Connected Convolutional Networks Gao Huang Zhuang Liu L. V. D. van der Maaten Kilian Q. Weinberger PINN 3DV 315 36,381 0 25 Aug 2016