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.

翻译：联邦学习(FL)是一种分布式机器学习(ML)处理方式,它保护用户数据的隐私,只与共用服务器共享ML模型的参数;FL的处理需要通信网络运作需要满足的具体的延时和带宽要求;本文介绍了用于服务质量的动态波长和带宽分配算法(Qos),为FL传输量超过50Gb/s Ethernet被动光学网络提供50Gb/s Ethernet被动光学网络;拟议的算法优先考虑FL通信量,减少FL的延迟,减少在同一基础设施支持的延迟关键应用。