Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/144299
Title: Hierarchical incentive mechanism design for federated machine learning in mobile networks
Authors: Lim, Bryan Wei Yang
Xiong, Zehui
Miao, Chunyan
Niyato, Dusit
Yang, Qiang
Leung, Cyril
Poor, H. Vincent
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Lim, B. W. Y., Xiong, Z., Miao, C., Niyato, D., Yang, Q., Leung, C., & Poor, H. V. (2020). Hierarchical incentive mechanism design for federated machine learning in mobile networks. IEEE Internet of Things Journal, 7(10), 9575-9588. doi:10.1109/JIOT.2020.2985694
Journal: IEEE Internet of Things Journal
Abstract: In recent years, the enhanced sensing and computation capabilities of Internet-of-Things (IoT) devices have opened the doors to several mobile crowdsensing applications. In mobile crowdsensing, a model owner announces a sensing task following which interested workers collect the required data. However, in some cases, a model owner may have insufficient data samples to build an effective machine learning model. To this end, we propose a federated learning (FL)-based privacy-preserving approach to facilitate collaborative machine learning among multiple model owners in mobile crowdsensing. Our system model allows collaborative machine learning without compromising data privacy given that only the model parameters instead of the raw data are exchanged within the federation. However, there are two main challenges of incentive mismatches between workers and model owners, as well as among model owners. For the former, we leverage on the self-revealing mechanism in the contract theory under information asymmetry. For the latter, to ensure the stability of a federation through preventing free-riding attacks, we use the coalitional game theory approach that rewards model owners based on their marginal contributions. Considering the inherent hierarchical structure of the involved entities, we propose a hierarchical incentive mechanism framework. Using the backward induction, we first solve the contract formulation and then proceed to solve the coalitional game with the merge and split algorithm. The numerical results validate the performance efficiency of our proposed hierarchical incentive mechanism design, in terms of incentive compatibility of our contract design and fair payoffs of model owners in stable federation formation.
URI: https://hdl.handle.net/10356/144299
ISSN: 2327-4662
DOI: 10.1109/JIOT.2020.2985694
Rights: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/JIOT.2020.2985694
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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