Federated Learning (FL) is a distributed machine learning (ML) type of processing that preserves the privacy of user data, sharing only the parameters of ML models with a common server. The processing of FL requires specific latency and bandwidth demands that need to be fulfilled by the operation of the communication network. This paper introduces a Dynamic Wavelength and Bandwidth Allocation algorithm for Quality of Service (QoS) provisioning for FL traffic over 50 Gb/s Ethernet Passive Optical Networks. The proposed algorithm prioritizes FL traffic and reduces the delay of FL and delay-critical applications supported on the same infrastructure.
@article{arxiv.2109.14593,
title = {Federated Learning over Next-Generation Ethernet Passive Optical Networks},
author = {Oscar J. Ciceri and Carlos A. Astudillo and Zuqing Zhu and Nelson L. S. da Fonseca},
journal= {arXiv preprint arXiv:2109.14593},
year = {2021}
}