Communication Trade-offs in Federated Learning of Spiking Neural Networks

27 February 2023

Papers citing "Communication Trade-offs in Federated Learning of Spiking Neural Networks"

9 / 9 papers shown

Title
Device Scheduling and Update Aggregation Policies for Asynchronous Federated Learning Chung-Hsuan Hu Zheng Chen Erik G. Larsson FedML 57 29 0 23 Jul 2021
Federated Learning with Spiking Neural Networks Yeshwanth Venkatesha Youngeun Kim Leandros Tassiulas Priyadarshini Panda FedML 60 48 0 11 Jun 2021
Federated learning with class imbalance reduction Miao Yang Akitanoshou Wong Hongbin Zhu Haifeng Wang H. Qian FedML 69 125 0 23 Nov 2020
Federated Neuromorphic Learning of Spiking Neural Networks for Low-Power Edge Intelligence N. Skatchkovsky Hyeryung Jang Osvaldo Simeone FedML 40 36 0 21 Oct 2019
The Heidelberg spiking datasets for the systematic evaluation of spiking neural networks Benjamin Cramer Yannik Stradmann Johannes Schemmel Friedemann Zenke 46 217 0 16 Oct 2019
A Joint Learning and Communications Framework for Federated Learning over Wireless Networks Mingzhe Chen Zhaohui Yang Walid Saad Changchuan Yin H. Vincent Poor Shuguang Cui FedML 96 1,192 0 17 Sep 2019
Surrogate Gradient Learning in Spiking Neural Networks Emre Neftci Hesham Mostafa Friedemann Zenke 104 1,242 0 28 Jan 2019
Federated Learning: Strategies for Improving Communication Efficiency Jakub Konecný H. B. McMahan Felix X. Yu Peter Richtárik A. Suresh Dave Bacon FedML 312 4,657 0 18 Oct 2016
Training Deep Spiking Neural Networks using Backpropagation Junhaeng Lee T. Delbruck Michael Pfeiffer 98 947 0 31 Aug 2016