@inproceedings{e611bcd9021a4d3084318f706508e01c,
title = "Wireless Federated Distillation for Distributed Edge Learning with Heterogeneous Data",
abstract = "Cooperative training methods for distributed machine learning typically assume noiseless and ideal communication channels. This work studies some of the opportunities and challenges arising from the presence of wireless communication links. We specifically consider wireless implementations of Federated Learning (FL) and Federated Distillation (FD), as well as of a novel Hybrid Federated Distillation (HFD) scheme. Both digital implementations based on separate source-channel coding and over-the-air computing implementations based on joint source-channel coding are proposed and evaluated over Gaussian multiple-access channels.",
author = "Ahn, {Jin Hyun} and Osvaldo Simeone and Joonhyuk Kang",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 30th IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2019 ; Conference date: 08-09-2019 Through 11-09-2019",
year = "2019",
month = sep,
doi = "10.1109/PIMRC.2019.8904164",
language = "English (US)",
series = "IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2019 IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2019",
address = "United States",
}