research-article

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

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Carole-Jean WuAuthors Info & Claims

RecSys '22: Proceedings of the 16th ACM Conference on Recommender Systems

Pages 156 - 167

https://doi.org/10.1145/3523227.3546759

Published: 13 September 2022 Publication History

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.

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Index Terms

Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity
1. Computing methodologies
  1. Modeling and simulation
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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RecSys '22: Proceedings of the 16th ACM Conference on Recommender Systems

September 2022

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ISBN:9781450392785

DOI:10.1145/3523227

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