Fair Collaborative Learning (FairCL): A Method to Improve Fairness amid Personalization

Feng Lin (), Chaoyue Zhao (), Xiaoning Qian (), Kendra Vehik () and Shuai Huang ()
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Feng Lin: Department of Industrial and Systems Engineering, University of Washington, Seattle, Washington 98195
Chaoyue Zhao: Department of Industrial and Systems Engineering, University of Washington, Seattle, Washington 98195
Xiaoning Qian: Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas 77843
Kendra Vehik: Health Informatics Institute, University of South Florida, Tampa, Florida 33620
Shuai Huang: Department of Industrial and Systems Engineering, University of Washington, Seattle, Washington 98195

INFORMS Joural on Data Science, 2025, vol. 4, issue 1, 67-84

Abstract: Model personalization has attracted widespread attention in recent years. In an ideal situation, if individuals’ data are sufficient, model personalization can be realized by building models separately for different individuals using their own data. But, in reality, individuals often have data sets of varying sizes and qualities. To overcome this disparity, collaborative learning has emerged as a generic strategy for model personalization, but there is no mechanism to ensure fairness in this framework. In this paper, we develop fair collaborative learning (FairCL) that could potentially integrate a variety of fairness concepts. We further focus on two specific fairness metrics, the bounded individual loss and individual fairness, and develop a self-adaptive algorithm for FairCL and conduct both simulated and real-world case studies. Our study reveals that model fairness and accuracy could be improved simultaneously in the context of model personalization.

Keywords: collaborative learning (CL); fairness; model personalization; disparity (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:inm:orijds:v:4:y:2025:i:1:p:67-84

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