Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/164431
Title: MarS-FL: enabling competitors to collaborate in federated learning
Authors: Wu, Xiaohu
Yu, Han
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Wu, X. & Yu, H. (2022). MarS-FL: enabling competitors to collaborate in federated learning. IEEE Transactions On Big Data, 1-11. https://dx.doi.org/10.1109/TBDATA.2022.3186991
Project: AISG2-RP-2020-019 
FCPNTU-RG-2021-014
NWJ-2020-008
NAP
A20G8b0102 
FCP-NTU-RG-2021- 014
Journal: IEEE Transactions on Big Data
Abstract: Federated learning (FL) is rapidly gaining popularity and enables multiple data owners (a.k.a. FL participants) to collaboratively train machine learning models in a privacy-preserving way. A key unaddressed scenario is that these FL participants are in a competitive market, where market shares represent their competitiveness. Although they are interested to enhance the performance of their respective models through FL, market leaders (who are often data owners who can contribute significantly to building high performance FL models) want to avoid losing their market shares by enhancing their competitors’ models. Currently, there is no modeling tool to analyze such scenarios and support informed decision-making. In this paper, we bridge this gap by proposing the market share-based decision support framework for participation in FL (MarS-FL). We introduce two notions of δ-stable market and friendliness to measure the viability of FL and the market acceptability of FL. The FL participants’ behaviours can then be predicted using game theoretic tools (i.e., their optimal strategies concerning participation in FL). If the market δ-stability is achievable, the final model performance improvement of each FL-PT shall be bounded, which relates to the market conditions of FL applications. We provide tight bounds and quantify the friendliness, κ, of given market conditions to FL. Experimental results show the viability of FL in a wide range of market conditions. Our results are useful for identifying the market conditions under which collaborative FL model training is viable among competitors, and the requirements that have to be imposed while applying FL under these conditions.
URI: https://hdl.handle.net/10356/164431
ISSN: 2332-7790
DOI: 10.1109/TBDATA.2022.3186991
Rights: © 2022 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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