Enhancing Group Fairness in Federated Learning through Personalization (2407.19331v2)

Abstract: Personalized Federated Learning (FL) algorithms collaboratively train customized models for each client, enhancing the accuracy of the learned models on the client's local data (e.g., by clustering similar clients, by fine-tuning models locally, or by imposing regularization terms). In this paper, we investigate the impact of such personalization techniques on the group fairness of the learned models, and show that personalization can also lead to improved (local) fairness as an unintended benefit. We begin by illustrating these benefits of personalization through numerical experiments comparing several classes of personalized FL algorithms against a baseline FedAvg algorithm, elaborating on the reasons behind improved fairness using personalized FL, and then providing analytical support. Motivated by these, we then show how to build on this (unintended) fairness benefit, by further integrating a fairness metric into the cluster-selection procedure of clustering-based personalized FL algorithms, and improve the fairness-accuracy trade-off attainable through them. Specifically, we propose two new fairness-aware federated clustering algorithms, Fair-FCA and Fair-FL+HC, extending the existing IFCA and FL+HC algorithms, and demonstrate their ability to strike a (tuneable) balance between accuracy and fairness at the client level.

Enhancing Group Fairness in Federated Learning Through Personalization: A Detailed Analysis

This paper investigates the interplay between personalization and group fairness in Federated Learning (FL), a decentralized learning paradigm that retains data privacy by enabling distributed data training. In typical FL, models are trained collaboratively across a diverse set of clients to build a robust global model. However, these models often fall short in customization for individual clients and can inadvertently neglect the data disparities among various demographic groups, leading to systemic biases. Addressing these challenges, the authors explore how personalization techniques, which are predominantly designed to enhance local accuracy, can simultaneously improve fairness, mitigating bias across different groups.

Main Contributions

1. Unintended Fairness Benefits: The authors demonstrate through extensive numerical experiments that personalization inadvertently enhances fairness. Surprisingly, techniques primarily aimed at optimizing local accuracy also contribute favorably to reducing fairness disparities. Key experiments—utilizing datasets like the "Adult" and "Retiring Adult"—illustrate the potential for a dual benefit in both accuracy and fairness, suggesting statistical diversity and computational alignment as contributing factors.
2. Fairness-Aware Federated Clustering Algorithms: Inspired by the unintended fairness benefits observed, the paper proposes two new algorithms: Fair-FCA and Fair-FL+HC. These are designed to weave a fairness metric into the client clustering process, optimizing both local model accuracy and fairness. By incorporating fairness considerations into the clustering mechanism, these algorithms achieve a tunable balance, providing a preferable trade-off between fairness and accuracy.
3. Statistical and Computational Insights: The research supports its findings with statistical analysis and computational insights. Under certain conditions, the paper posits that personalized and clustered FL models better align accuracy and fairness objectives, offering empirical evidence that personalization reduces overfitting tendencies to the majority data.

Implications and Future Directions

The implications of this work are significant; they indicate a path forward where federated personalization not only addresses client-specific accuracy needs but also promotes social fairness without additional fairness constraints. This discovery opens new avenues for developing fairness-centric personalized algorithms that can adaptively balance dual objectives within FL frameworks.

From a theoretical perspective, the paper's analytical support suggests that the conditions under which personalization improves fairness can inform the design of future personalized FL systems. Future work could extend these insights to other classes of personalized FL methods beyond clustering-based approaches and investigate leveraging these findings in real-world applications where fairness is crucial, such as in healthcare and finance.

Overall, this paper provides a structured examination of personalization's role in advancing fairness in FL, offering a compelling narrative supported by empirical data and newly proposed methodologies. Through its dual-focused algorithms, it sets a precedent for the integration of fairness and personalization, fostering a fairer and more efficient federated learning paradigm.