Cited by 602Privacy-Preserving Technologies in DataIoT and Edge/Fog ComputingBlockchain Technology Applications and Security

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A Learning-Based Incentive Mechanism for Federated Learning

Zhan, Yufeng, Li, Peng, Qu, Zhihao, Zeng, Deze, Guo, Song·2020·IEEE Internet of Things Journal

DOI 10.1109/jiot.2020.2967772

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Abstract

Internet of Things (IoT) generates large amounts of data at the network edge. Machine learning models are often built on these data, to enable the detection, classification, and prediction of the future events. Due to network bandwidth, storage, and especially privacy concerns, it is often impossible to send all the IoT data to the data center for centralized model training. To address these issues, federated learning has been proposed to let nodes use the local data to train models, which are then aggregated to synthesize a global model. Most of the existing work has focused on designing learning algorithms with provable convergence time, but other issues, such as incentive mechanism, are unexplored. Although incentive mechanisms have been extensively studied in network and computation resource allocation, yet they cannot be applied to federated learning directly due to the unique challenges of information unsharing and difficulties of contribution evaluation. In this article, we study the incentive mechanism for federated learning to motivate edge nodes to contribute model training. Specifically, a deep reinforcement learning-based (DRL) incentive mechanism has been designed to determine the optimal pricing strategy for the parameter server and the optimal training strategies for edge nodes. Finally, numerical experiments have been implemented to evaluate the efficiency of the proposed DRL-based incentive mechanism.

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Authors: Zhan, Y., Li, P., Qu, Z., Zeng, D., & Guo, S.
Affiliations: Hong Kong Polytechnic University (HK)
University of Aizu (JP)
China University of Geosciences (CN)
ORCID: Zhan, Yufeng
Li, Peng
Qu, Zhihao
Zeng, Deze
Guo, Song
Venue: IEEE Internet of Things Journal
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2020-07
Volume: 7
Issue: 7
Pages: 6360-6368
ISSN: 2327-4662 (ISSN-L)
2372-2541
DOI: 10.1109/jiot.2020.2967772
OpenAlex: W3001989995
Semantic Scholar: 08a9c7ba8868ad41bcf911ea389c8dc184922627
Cited by: 602
References: 47
Citations by year: 2026: 40 · 2025: 81 · 2024: 128 · 2023: 128 · 2022: 108 · 2021: 86 · 2020: 31
FWCI: 34.67
Type: journal article
Language: en
Open access: closed
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Internet of Things Journal (publishedVersion)
Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
Funders: General Research Fund of the Research Grants Council of Hong Kong (PolyU 152221/19E)
National Natural Science Foundation of China (61872310)
JSPS Grants-in-Aid for Scientific Research (JP19K20258)
Basic Research-Free Exploration Project of Shenzhen (JCYJ20170818103849343)
China Postdoctoral Science Foundation (2019M661709)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
Keywords: federated learning, incentive mechanisms, deep reinforcement learning, Internet of Things, edge computing, privacy-preserving training

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A Learning-Based Incentive Mechanism for Federated Learning

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