FairFed: Enabling Group Fairness in Federated Learning

By Yahya H. Ezzeldin, Shen Yan, Chaoyang He, Emilio Ferrara, Amir Salman Avestimehr. AAAI Conference on Artificial Intelligence, 2021

324 citations recorded by the source corpus.

Training ML models which are fair across different demographic groups is of critical importance due to the increased integration of ML in crucial decision-making scenarios such as healthcare and recruitment. Federated learning has been viewed as a promising solution for collaboratively training machine learning models among multiple parties while maintaining their local data privacy. However, federated learning also poses new challenges in mitigating the potential bias against certain populations (e.g., demographic groups), as this typically requires centralized access to the sensitive information (e.g., race, gender) of each datapoint. Motivated by the importance and challenges of group fairness in federated learning, in this work, we propose FairFed, a novel algorithm for fairness-aware aggregation to enhance group fairness in federated learning. Our proposed approach is server-side and agnostic to the applied local debiasing thus allowing for flexible use of different local debiasing methods across clients. We evaluate FairFed empirically versus common baselines for fair ML and federated learning and demonstrate that it provides fairer models, particularly under highly heterogeneous data distributions across clients. We also demonstrate the benefits of FairFed in scenarios involving naturally distributed real-life data collected from different geographical locations or departments within an organization.

Subfields: Artificial Intelligence, Computer Science Applications, Safety Research

View Paper: https://www.semanticscholar.org/paper/e697cfd2694a815f42e085150dffaaf65acfa17e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

  1. María Victoria Luzón
  2. Nuria Rodr'iguez-Barroso
  3. Francisco Herrera
  4. Daniel Jim'enez L'opez
  5. Eugenio Martínez-Cámara
  6. Emilio Ferrara
  7. David M. Beskow
  8. Filippo Menczer

In Computer Science Applications

  1. Emilio Ferrara

In Safety Research

  1. Emilio Ferrara

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