Document Type
Conference Proceeding
Publication Date
10-16-2024
Journal / Book Title
Frontiers in Artificial Intelligence and Applications
Abstract
In cross-silo federated learning (FL), companies collaboratively train a shared global model without sharing heterogeneous data. Prior related work focused on algorithm development to tackle data heterogeneity. However, the dual problem of coopetition, i.e., FL collaboration and market competition, remains under-explored. This paper studies the FL coopetition using a dynamic two-period game model. In period 1, an incumbent company trains a local model and provides model-based services at a chosen price to users. In period 2, an entrant company enters, and both companies decide whether to engage in FL collaboration and then compete in selling model-based services at different prices to users. Analyzing the two-period game is challenging due to data heterogeneity, and that the incumbent's period one pricing has a temporal impact on coopetition in period 2, resulting in a non-concave problem. To address this issue, we decompose the problem into several concave sub-problems and develop an algorithm that achieves a global optimum. Numerical results on three public datasets show two interesting insights. First, FL training brings model performance gain as well as competition loss, and collaboration occurs only when the performance gain outweighs the loss. Second, data heterogeneity can incentivize the incumbent to limit market penetration in period 1 and promote price competition in period 2.
DOI
10.3233/FAIA240776
Journal ISSN / Book ISBN
85213313278 (Scopus)
Montclair State University Digital Commons Citation
Huang, Chao; Dachille, Justin; and Liu, Xin, "Coopetition in Heterogeneous Cross-Silo Federated Learning" (2024). School of Computing Faculty Scholarship and Creative Works. 32.
https://digitalcommons.montclair.edu/computing-facpubs/32
Published Citation
Huang, C., Dachille, J., & Liu, X. (2024). Coopetition in heterogeneous cross-silo federated learning. In U. Endriss, F. S. Melo, K. Bach, A. Bugarín-Diz, J. M. Alonso-Moral, S. Barro, & F. Heintz (Eds.), Frontiers in Artificial Intelligence and Applications. IOS Press. https://doi.org/10.3233/FAIA240776
Comments
This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).