FIARSE: Model-Heterogeneous Federated Learning via Importance-Aware
Submodel Extraction

FIARSE: Model-Heterogeneous Federated Learning via Importance-Aware Submodel Extraction

28 July 2024

Tianci Liu

Jing Gao

Papers citing "FIARSE: Model-Heterogeneous Federated Learning via Importance-Aware Submodel Extraction"

8 / 8 papers shown

Title
FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architectures Jiacheng Wang Hongtao Lv Lei Liu FedML 25 0 0 10 May 2025
FedFetch: Faster Federated Learning with Adaptive Downstream Prefetching Qifan Yan Andrew Liu Shiqi He Mathias Lécuyer Ivan Beschastnikh FedML 36 0 0 21 Apr 2025
FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity Kai Yi Nidham Gazagnadou Peter Richtárik Lingjuan Lyu 79 11 0 15 Apr 2024
Sparse Random Networks for Communication-Efficient Federated Learning Berivan Isik Francesco Pase Deniz Gunduz Tsachy Weissman M. Zorzi FedML 70 52 0 30 Sep 2022
A Field Guide to Federated Optimization Jianyu Wang Zachary B. Charles Zheng Xu Gauri Joshi H. B. McMahan ... Mi Zhang Tong Zhang Chunxiang Zheng Chen Zhu Wennan Zhu FedML 187 411 0 14 Jul 2021
Communication-Efficient and Personalized Federated Lottery Ticket Learning Sejin Seo Seung-Woo Ko Jihong Park Seong-Lyun Kim M. Bennis FedML 47 15 0 26 Apr 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 186 267 0 26 Feb 2021
Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks Torsten Hoefler Dan Alistarh Tal Ben-Nun Nikoli Dryden Alexandra Peste MQ 141 684 0 31 Jan 2021