Enabling Awareness of Quality of Training and Costs in Federated Machine Learning Marketplaces

Tien Dung Cao*, Hong Linh Truong, Tram Truong-Huu, Minh Tri Nguyen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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Abstract

The proliferation of data and machine learning (ML) as a service, coupled with advanced federated and distributed training techniques, fosters the development of federated ML marketplaces. One important, but under-researched, aspect is to enable the stakeholder interactions centered around the quality of training and costs in the marketplace and the service models in federated ML training. This paper conceptualizes a federated ML marketplace and proposes a framework to enable the awareness of the quality of training and a variety of costs where both data providers and ML model consumers can easily value the contribution of each data source to ML model performance in nearly real-time. This improves the transparency and explainability iv.r.t. the quality and costs for participation in the marketplace. Based on that, we design and implement the quality of training and cost awareness framework for an edge federated ML marketplace. Experimental realistic scenarios show the usefulness of cost and quality details that provides insightful information for various purposes, including potential budget management and training optimization.

Original languageEnglish
Title of host publicationProceedings - 2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing, UCC 2022
PublisherIEEE
Pages41-50
Number of pages10
ISBN (Electronic)978-1-6654-6087-3
DOIs
Publication statusPublished - 2022
MoE publication typeA4 Conference publication
EventIEEE/ACM International Conference on Utility and Cloud Computing - Vancouver, United States
Duration: 6 Dec 20229 Dec 2022
Conference number: 15

Conference

ConferenceIEEE/ACM International Conference on Utility and Cloud Computing
Abbreviated titleUCC
Country/TerritoryUnited States
CityVancouver
Period06/12/202209/12/2022

Keywords

  • Cost Evaluation
  • Federated Learning
  • Marketplaces
  • ML as a Service
  • Observability

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