Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity

Kiwan Maeng, Haiyu Lu, Luca Melis, John Nguyen, Mike Rabbat, Carole Jean Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Federated learning (FL) is an effective mechanism for data privacy in recommender systems that runs machine learning model training on-device. While prior FL optimizations tackled the data and system heterogeneity challenges, they assume the two are independent of each other. This fundamental assumption is not reflective of real-world, large-scale recommender systems-data and system heterogeneity are tightly intertwined. This paper takes a data-driven approach to show the inter-dependence of data and system heterogeneity in real-world data and quantifies its impact on the overall model quality and fairness. We design a framework, RF2, to model the inter-dependence and evaluate its impact on state-of-the-art model optimization techniques for federated recommendation tasks. We demonstrate that the impact on fairness can be severe under realistic heterogeneity scenarios, by up to 15.8-41 × compared to a simple setup assumed in most (if not all) prior work. The result shows that modeling realistic system-induced data heterogeneity is essential to achieving fair federated recommendation learning.

Original languageEnglish (US)
Title of host publicationRecSys 2022 - Proceedings of the 16th ACM Conference on Recommender Systems
PublisherAssociation for Computing Machinery, Inc
Pages156-167
Number of pages12
ISBN (Electronic)9781450392785
DOIs
StatePublished - Sep 12 2022
Event16th ACM Conference on Recommender Systems, RecSys 2022 - Seattle, United States
Duration: Sep 18 2022Sep 23 2022

Publication series

NameRecSys 2022 - Proceedings of the 16th ACM Conference on Recommender Systems

Conference

Conference16th ACM Conference on Recommender Systems, RecSys 2022
Country/TerritoryUnited States
CitySeattle
Period9/18/229/23/22

ASJC Scopus subject areas

  • Hardware and Architecture
  • Software

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