The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
A Learning-Based Incentive Mechanism for Federated Learning
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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In the citing paper
“FTL was first proposed in [71].”
From Challenges, Applications and Design Aspects of Federated Learning: A Survey (2021) · cites this paper as [71]
In the citing paper
“Most of the state-of-the art techniques [40], [41] use DRL to implement resource allocation strategies for FLchain systems under energy and channel bandwidth availability conditions.”
From Federated learning meets blockchain in edge computing: Opportunities and challenges (2021) · cites this paper as [41]
In the citing paper
“Without a proper incentive mechanism, clients may not be willing to join the data training which would reduce the scalability of the designed FL system [45].”
From Federated learning meets blockchain in edge computing: Opportunities and challenges (2021) · cites this paper as [45]
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Metadata
- 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)
- Venue
- IEEE Internet of Things Journal
- Published
- 2020-07
- Volume
- 7
- Issue
- 7
- Pages
- 6360-6368
- ISSN
- 2327-4662 (ISSN-L)
- 2372-2541
- 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
- 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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