The analysis of data coming from massively deployed Internet of Things (IoT) devices pave the way to a myriad of intelligent applications in several vertical domains. Federated Learning (FL) has been recently proposed as a prominent solution to train Machine Learning (ML) models directly on top of devices (FL clients) generating data, instead of moving them to centralized servers in charge of training procedures. FL provides inherent benefits, mainly in terms of privacy preservation and reduction of network congestion for datasets exchange. Despite the huge recent research efforts, it still faces challenges for a practical implementation effectively targeting low communication footprint, robustness and interoperability. To fill this gap, in this work we propose a novel comprehensive framework built upon the Message Queue Telemetry Transport (MQTT) publish/subscribe messaging protocol and the Open Mobile Alliance (OMA) Lightweight Machine-to-Machine (LwM2M) semantics to facilitate FL operations and make them more suited to handle IoT devices acting as FL clients. The viability of the proposal as well as its communication efficiency compared to a literature solution are evaluated through a realistic Proof-of-Concept (PoC) under different link settings and for different datasets.

Scalable and interoperable edge-based federated learning in IoT contexts

Campolo C.
;
Singh G.;Molinaro A.
2023-01-01

Abstract

The analysis of data coming from massively deployed Internet of Things (IoT) devices pave the way to a myriad of intelligent applications in several vertical domains. Federated Learning (FL) has been recently proposed as a prominent solution to train Machine Learning (ML) models directly on top of devices (FL clients) generating data, instead of moving them to centralized servers in charge of training procedures. FL provides inherent benefits, mainly in terms of privacy preservation and reduction of network congestion for datasets exchange. Despite the huge recent research efforts, it still faces challenges for a practical implementation effectively targeting low communication footprint, robustness and interoperability. To fill this gap, in this work we propose a novel comprehensive framework built upon the Message Queue Telemetry Transport (MQTT) publish/subscribe messaging protocol and the Open Mobile Alliance (OMA) Lightweight Machine-to-Machine (LwM2M) semantics to facilitate FL operations and make them more suited to handle IoT devices acting as FL clients. The viability of the proposal as well as its communication efficiency compared to a literature solution are evaluated through a realistic Proof-of-Concept (PoC) under different link settings and for different datasets.
2023
Federated learning
MQTT
OMA LwM2M
File in questo prodotto:
File Dimensione Formato  
CAMPOLO_2023_COMNET_SCALABLE_postPrint.pdf

embargo fino al 14/01/2025

Descrizione: Post-print
Tipologia: Documento in Post-print
Licenza: Creative commons
Dimensione 1.24 MB
Formato Adobe PDF
1.24 MB Adobe PDF   Visualizza/Apri   Richiedi una copia
CAMPOLO_2023_COMNET_SCALABLE_editorial.pdf

non disponibili

Descrizione: Versione editoriale
Tipologia: Versione Editoriale (PDF)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 2.05 MB
Formato Adobe PDF
2.05 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12318/133756
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 9
  • ???jsp.display-item.citation.isi??? 4
social impact