ProgFed: Effective, Communication, and Computation Efficient Federated
Learning by Progressive Training

ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training

11 October 2021

Sebastian U. Stich

Mario Fritz

Papers citing "ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training"

15 / 15 papers shown

Title
Efficient Zero-Order Federated Finetuning of Language Models for Resource-Constrained Devices Mohamed Aboelenien Ahmed Kilian Pfeiffer R. Khalili Heba Khdr J. Henkel FedML 87 0 0 17 Feb 2025
Efficient Federated Finetuning of Tiny Transformers with Resource-Constrained Devices Kilian Pfeiffer Mohamed Aboelenien Ahmed R. Khalili J. Henkel 30 0 0 12 Nov 2024
Language Models as Zero-shot Lossless Gradient Compressors: Towards General Neural Parameter Prior Models Hui-Po Wang Mario Fritz 33 3 0 26 Sep 2024
Resource-Efficient Federated Multimodal Learning via Layer-wise and Progressive Training Ye Lin Tun Chu Myaet Thwal Minh N. H. Nguyen Choong Seon Hong 36 0 0 22 Jul 2024
A Survey on Federated Analytics: Taxonomy, Enabling Techniques, Applications and Open Issues Zibo Wang Haichao Ji Yifei Zhu Dan Wang Zhu Han 43 1 0 19 Apr 2024
LW-FedSSL: Resource-efficient Layer-wise Federated Self-supervised Learning Ye Lin Tun Chu Myaet Thwal Le Quang Huy Minh N. H. Nguyen Choong Seon Hong FedML 38 2 0 22 Jan 2024
Heterogeneous Federated Learning: State-of-the-art and Research Challenges Mang Ye Xiuwen Fang Bo Du PongChi Yuen Dacheng Tao FedML AAML 29 244 0 20 Jul 2023
Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devices Kilian Pfeiffer R. Khalili J. Henkel FedML 33 5 0 26 May 2023
Does Federated Learning Really Need Backpropagation? H. Feng Tianyu Pang Chao Du Wei-Neng Chen Shuicheng Yan Min-Bin Lin FedML 22 10 0 28 Jan 2023
FedCliP: Federated Learning with Client Pruning Beibei Li Zerui Shao Ao Liu Peiran Wang FedML 37 1 0 17 Jan 2023
Federated Learning for Inference at Anytime and Anywhere Zicheng Liu Da Li Javier Fernandez-Marques Stefanos Laskaridis Yan Gao L. Dudziak Stan Z. Li S. Hu Timothy M. Hospedales FedML 16 5 0 08 Dec 2022
FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers Jinyu Chen Wenchao Xu Song Guo Junxiao Wang Jie M. Zhang Haozhao Wang FedML 15 32 0 15 Nov 2022
Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning Shaoxiong Ji Yue Tan Teemu Saravirta Zhiqin Yang Yixin Liu Lauri Vasankari Shirui Pan Guodong Long A. Walid FedML 21 76 0 25 Feb 2021
CaPC Learning: Confidential and Private Collaborative Learning Christopher A. Choquette-Choo Natalie Dullerud Adam Dziedzic Yunxiang Zhang S. Jha Nicolas Papernot Xiao Wang FedML 59 57 0 09 Feb 2021
Cascade EF-GAN: Progressive Facial Expression Editing with Local Focuses R. Wu Gongjie Zhang Shijian Lu Tao Chen CVBM 22 95 0 12 Mar 2020