  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.
