Bandwidth Slicing to Boost Federated Learning over Passive Optical Networks
Artikel i vetenskaplig tidskrift, 2020

During federated learning (FL) process, each client needs to periodically upload local model parameters and download global model parameters to/from the central server, thus requires efficient communications. Meanwhile, passive optical network (PON) is promising to support fog computing where FL tasks can be executed and the traffic generated by FL needs to be transmitted together with other types of traffic for broadband access. In this letter, a bandwidth slicing algorithm in PONs is introduced for efficient FL, in which bandwidth is reserved for the involved ONUs collaboratively and mapped into each polling cycle. Results reveal that the proposed bandwidth slicing significantly improves training efficiency while achieving good learning accuracy for the FL task running over the PON.

Bandwidth slicing

federated learning

fog computing

passive optical networks

Författare

Jun Li

Chalmers, Elektroteknik, Kommunikations- och antennsystem, Optiska nätverk

Xiaoman Shen

Zhejiang University

Lei Chen

RISE Research Institutes of Sweden

Jiajia Chen

Chalmers, Elektroteknik, Kommunikations- och antennsystem, Optiska nätverk

IEEE Communications Letters

1089-7798 (ISSN)

Vol. 24 7 1492-1495 9044640

Ämneskategorier

Datorteknik

Kommunikationssystem

Datavetenskap (datalogi)

DOI

10.1109/LCOMM.2020.2982397

Mer information

Senast uppdaterat

2020-09-28