Federated Learning over Next-Generation Ethernet Passive Optical Networks

Oscar J. Ciceri; Carlos A. Astudillo; Zuqing Zhu; Nelson L. S. da Fonseca

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arXiv · 2109.14593

Federated Learning over Next-Generation Ethernet Passive Optical Networks

Oscar J. Ciceri·Carlos A. Astudillo·Zuqing Zhu·Nelson L. S. da Fonseca

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Abstract

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.

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BibTeX RIS

Oscar J. Ciceri, Carlos A. Astudillo, Zuqing Zhu, Nelson L. S. da Fonseca. 2021-09-29. Federated Learning over Next-Generation Ethernet Passive Optical Networks. https://arxiv.org/abs/2109.14593

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