The paper introduces Opportunistic Federated Learning (OFL) as an approach to enhance the efficiency of distributed learning in intelligent IoT systems. OFL allows any node in the network to initiate a learning task and collabo- ratively use local resources. The framework enables nodes to adapt configurations based on circumstances, optimizing resource utilization. Hence, this paper proposes a reliable node selection mechanism that accommodates the dynamic nature of local data and computing resources. Incentives for participating nodes are explored through a peer-to-peer communication using the Bertrand game to determine optimal pricing strategies. Results demonstrate the Nash equilibrium of the game-based incentive mechanism in a realistic FL setup.

Edge-assisted Opportunistic Federated Learning for Distributed IoT systems / Khial, Noor; Awad Abdellatif, Alaa; Mohamed, Amr; Erbad, Aiman; Chiasserini, Carla Fabiana. - (2024). (Intervento presentato al convegno IEEE Consumer Communications & Networking Conference (IEEE CCNC 2024) tenutosi a Las Vegas (USA) nel 6–9 January 2024).

Edge-assisted Opportunistic Federated Learning for Distributed IoT systems

Carla Fabiana Chiasserini
2024

Abstract

The paper introduces Opportunistic Federated Learning (OFL) as an approach to enhance the efficiency of distributed learning in intelligent IoT systems. OFL allows any node in the network to initiate a learning task and collabo- ratively use local resources. The framework enables nodes to adapt configurations based on circumstances, optimizing resource utilization. Hence, this paper proposes a reliable node selection mechanism that accommodates the dynamic nature of local data and computing resources. Incentives for participating nodes are explored through a peer-to-peer communication using the Bertrand game to determine optimal pricing strategies. Results demonstrate the Nash equilibrium of the game-based incentive mechanism in a realistic FL setup.
2024
File in questo prodotto:
File Dimensione Formato  
Game_Theoretical_Approach_for_Opportunistic_Federated_Learning_in_Distributed_IoT_systems_summary_paper.pdf

non disponibili

Tipologia: 2. Post-print / Author's Accepted Manuscript
Licenza: Non Pubblico - Accesso privato/ristretto
Dimensione 1.62 MB
Formato Adobe PDF
1.62 MB Adobe PDF   Visualizza/Apri   Richiedi una copia
Pubblicazioni consigliate

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/11583/2984016