CaBaFL: Asynchronous Federated Learning via Hierarchical Cache and
Feature Balance

CaBaFL: Asynchronous Federated Learning via Hierarchical Cache and Feature Balance

19 April 2024

Ming Hu

Anran Li

Papers citing "CaBaFL: Asynchronous Federated Learning via Hierarchical Cache and Feature Balance"

7 / 7 papers shown

Title
AdaptiveFL: Adaptive Heterogeneous Federated Learning for Resource-Constrained AIoT Systems Chentao Jia Ming Hu Zekai Chen Yanxin Yang Xiaofei Xie Yang Liu Mingsong Chen 60 7 0 22 Nov 2023
GitFL: Adaptive Asynchronous Federated Learning using Version Control Ming Hu Zeke Xia Zhihao Yue Jun Xia Yihao Huang Yang Liu Mingsong Chen FedML 83 15 0 22 Nov 2022
PyTorch: An Imperative Style, High-Performance Deep Learning Library Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury ... Sasank Chilamkurthy Benoit Steiner Lu Fang Junjie Bai Soumith Chintala ODL 292 42,038 0 03 Dec 2019
Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification T. Hsu Qi Matthew Brown FedML 111 1,128 0 13 Sep 2019
Robust and Communication-Efficient Federated Learning from Non-IID Data Felix Sattler Simon Wiedemann K. Müller Wojciech Samek FedML 47 1,343 0 07 Mar 2019
LEAF: A Benchmark for Federated Settings S. Caldas Sai Meher Karthik Duddu Peter Wu Tian Li Jakub Konecný H. B. McMahan Virginia Smith Ameet Talwalkar FedML 109 1,410 0 03 Dec 2018
Communication-Efficient Learning of Deep Networks from Decentralized Data H. B. McMahan Eider Moore Daniel Ramage S. Hampson Blaise Agüera y Arcas FedML 239 17,328 0 17 Feb 2016

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