2016
Kamp, Michael; Bothe, Sebastian; Boley, Mario; Mock, Michael
Communication-Efficient Distributed Online Learning with Kernels Proceedings Article
In: Frasconi, Paolo; Landwehr, Niels; Manco, Giuseppe; Vreeken, Jilles (Ed.): Machine Learning and Knowledge Discovery in Databases, pp. 805–819, Springer International Publishing, 2016.
Abstract | Links | BibTeX | Tags: communication-efficient, distributed, dynamic averaging, federated learning, kernel methods, parallelization
@inproceedings{kamp2016communication,
title = {Communication-Efficient Distributed Online Learning with Kernels},
author = {Michael Kamp and Sebastian Bothe and Mario Boley and Michael Mock},
editor = {Paolo Frasconi and Niels Landwehr and Giuseppe Manco and Jilles Vreeken},
url = {http://michaelkamp.org/wp-content/uploads/2020/03/Paper467.pdf},
year = {2016},
date = {2016-09-16},
urldate = {2016-09-16},
booktitle = {Machine Learning and Knowledge Discovery in Databases},
pages = {805--819},
publisher = {Springer International Publishing},
abstract = {We propose an efficient distributed online learning protocol for low-latency real-time services. It extends a previously presented protocol to kernelized online learners that represent their models by a support vector expansion. While such learners often achieve higher predictive performance than their linear counterparts, communicating the support vector expansions becomes inefficient for large numbers of support vectors. The proposed extension allows for a larger class of online learning algorithms—including those alleviating the problem above through model compression. In addition, we characterize the quality of the proposed protocol by introducing a novel criterion that requires the communication to be bounded by the loss suffered.},
keywords = {communication-efficient, distributed, dynamic averaging, federated learning, kernel methods, parallelization},
pubstate = {published},
tppubtype = {inproceedings}
}
We propose an efficient distributed online learning protocol for low-latency real-time services. It extends a previously presented protocol to kernelized online learners that represent their models by a support vector expansion. While such learners often achieve higher predictive performance than their linear counterparts, communicating the support vector expansions becomes inefficient for large numbers of support vectors. The proposed extension allows for a larger class of online learning algorithms—including those alleviating the problem above through model compression. In addition, we characterize the quality of the proposed protocol by introducing a novel criterion that requires the communication to be bounded by the loss suffered.
