Straggler-resilient Federated Learning: Tackling Computation Heterogeneity with Layer-wise Partial Model Training in Mobile Edge Network

16 November 2023

Papers citing "Straggler-resilient Federated Learning: Tackling Computation Heterogeneity with Layer-wise Partial Model Training in Mobile Edge Network"

6 / 6 papers shown

Title
Joint Superposition Coding and Training for Federated Learning over Multi-Width Neural Networks Hankyul Baek Won Joon Yun Yunseok Kwak Soyi Jung Mingyue Ji M. Bennis Jihong Park Joongheon Kim FedML 62 20 0 05 Dec 2021
Federated Dropout -- A Simple Approach for Enabling Federated Learning on Resource Constrained Devices Dingzhu Wen Ki-Jun Jeon Kaibin Huang FedML 68 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 176 267 0 26 Feb 2021
Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edge M. Amiri Deniz Gunduz Sanjeev R. Kulkarni H. Vincent Poor 71 169 0 28 Jan 2020
Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML Aniruddh Raghu M. Raghu Samy Bengio Oriol Vinyals 172 639 0 19 Sep 2019
Adaptive Federated Learning in Resource Constrained Edge Computing Systems Shiqiang Wang Tiffany Tuor Theodoros Salonidis K. Leung C. Makaya T. He Kevin S. Chan 144 1,685 0 14 Apr 2018