@inproceedings{0ffdee3515e74f329b6243881d219de5,
title = "Edge-Assisted Opportunistic Federated Learning for Distributed IoT Systems",
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 collaboratively 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.",
keywords = "Distributed learning, Nash equilibrium, edge computing, game theory, reputation analysis",
author = "Noor Khial and Abdellatif, \{Alaa Awad\} and Amr Mohamed and Aiman Erbad and Chiasserini, \{Carla Fabiana\}",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 21st IEEE Consumer Communications and Networking Conference, CCNC 2024 ; Conference date: 06-01-2024 Through 09-01-2024",
year = "2024",
month = mar,
day = "18",
doi = "10.1109/CCNC51664.2024.10454883",
language = "English",
series = "Proceedings - IEEE Consumer Communications and Networking Conference, CCNC",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "604--605",
booktitle = "2024 IEEE 21st Consumer Communications and Networking Conference, CCNC 2024",
address = "United States",
}