Stakeholder-oriented Decision Support for Auction-based Federated Learning

Xiaoli Tang

IJCAI 2024poster7 citations

Stakeholder-oriented Decision Support for Auction-based Federated Learning

Abstract

Auction-based federated learning (AFL) is an important area of FL incentive mechanism design. It effectively incentivizes high-quality data owners (DOs) to participate in data consumers' (DCs, i.e., servers') FL training tasks. However, AFL is still evolving, with existing methods primarily addressing optimal DC-DO matching or DC selection problems in monopoly markets. To enhance the practicality of AFL, we introduce stakeholder-oriented decision support in AFL. This facilitates optimal and strategic decision-making for all stakeholders, improving the efficiency and sustainability of the AFL ecosystem.

DC: Machine Learning

BibTeX

@inproceedings{ijcai2024p972,
  title     = {Stakeholder-oriented Decision Support for Auction-based Federated Learning},
  author    = {Tang, Xiaoli},
  booktitle = {Proceedings of the Thirty-Third International Joint Conference on
               Artificial Intelligence, {IJCAI-24}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Kate Larson},
  pages     = {8514--8515},
  year      = {2024},
  month     = {8},
  note      = {Doctoral Consortium},
  doi       = {10.24963/ijcai.2024/972},
  url       = {https://doi.org/10.24963/ijcai.2024/972},
}