Summary
Asia-Pacific Network Operations and Management Symposium
2022
Session Number:PS2
Session:
Number:PS2-05
An Encouraging Design for Data Owners to Join Multiple Co-Existing Federated Learning
Loc X.Nguyen, Luyao Zou, Huy Q.Le, Choong Seon Hong,
pp.-
Publication Date:2022/09/28
Online ISSN:2188-5079
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Summary:
Federated learning is a distributed learning system that addresses the distributed difficulty such as communication overhead and private information in machine learning while maintaining high performance. However, the distributed learners have to dedicate their resources to improving the global model, which is not likely to happen voluntarily. This motivated us to design an incentive mechanism for users (data owners) to actively participate in the FL processes. In this paper, we consider multiple co-existing FL service providers (FLSPs) with the need to train their models and multiple data owners (DOs) that can offer that service. In the system, DO, and FLSP will submit their cost and valuation values to the cloud platform. Based on this information, we formulate an optimization problem that aims to maximize the social welfare under the nonnegative utility constraint and maximum gain of FLSPs. Then,