Enhancing Group Fairness in Federated Learning through Personalization

• URL: http://arxiv.org/abs/2407.19331v2
• Date: Thu, 3 Oct 2024 00:29:33 GMT
• Title: Enhancing Group Fairness in Federated Learning through Personalization
• Authors: Yifan Yang, Ali Payani, Parinaz Naghizadeh,
• Abstract summary: We show that personalization can lead to improved (local) fairness as an unintended benefit. We propose two new fairness-aware clustering algorithms, Fair-FCA and Fair-FL+HC.
• Score: 15.367801388932145
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Interaction-Aware Gaussian Weighting for Clustered Federated Learning [58.92159838586751]
  Federated Learning (FL) emerged as a decentralized paradigm to train models while preserving privacy. We propose a novel clustered FL method, FedGWC (Federated Gaussian Weighting Clustering), which groups clients based on their data distribution. Our experiments on benchmark datasets show that FedGWC outperforms existing FL algorithms in cluster quality and classification accuracy.
  arXiv  Detail & Related papers  (2025-02-05T16:33:36Z)
• Client-Centric Federated Adaptive Optimization [78.30827455292827]
  Federated Learning (FL) is a distributed learning paradigm where clients collaboratively train a model while keeping their own data private. We propose Federated-Centric Adaptive Optimization, which is a class of novel federated optimization approaches.
  arXiv  Detail & Related papers  (2025-01-17T04:00:50Z)
• Over-the-Air Fair Federated Learning via Multi-Objective Optimization [52.295563400314094]
  We propose an over-the-air fair federated learning algorithm (OTA-FFL) to train fair FL models. Experiments demonstrate the superiority of OTA-FFL in achieving fairness and robust performance.
  arXiv  Detail & Related papers  (2025-01-06T21:16:51Z)
• Personalized Federated Learning via Feature Distribution Adaptation [3.410799378893257]
  Federated learning (FL) is a distributed learning framework that leverages commonalities between distributed client datasets to train a global model. personalized federated learning (PFL) seeks to address this by learning individual models tailored to each client. We propose an algorithm, pFedFDA, that efficiently generates personalized models by adapting global generative classifiers to their local feature distributions.
  arXiv  Detail & Related papers  (2024-11-01T03:03:52Z)
• An Aggregation-Free Federated Learning for Tackling Data Heterogeneity [50.44021981013037]
  Federated Learning (FL) relies on the effectiveness of utilizing knowledge from distributed datasets. Traditional FL methods adopt an aggregate-then-adapt framework, where clients update local models based on a global model aggregated by the server from the previous training round. We introduce FedAF, a novel aggregation-free FL algorithm.
  arXiv  Detail & Related papers  (2024-04-29T05:55:23Z)
• Federated Learning Can Find Friends That Are Advantageous [14.993730469216546]
  In Federated Learning (FL), the distributed nature and heterogeneity of client data present both opportunities and challenges. We introduce a novel algorithm that assigns adaptive aggregation weights to clients participating in FL training, identifying those with data distributions most conducive to a specific learning objective.
  arXiv  Detail & Related papers  (2024-02-07T17:46:37Z)
• Contrastive encoder pre-training-based clustered federated learning for heterogeneous data [17.580390632874046]
  Federated learning (FL) enables distributed clients to collaboratively train a global model while preserving their data privacy. We propose contrastive pre-training-based clustered federated learning (CP-CFL) to improve the model convergence and overall performance of FL systems.
  arXiv  Detail & Related papers  (2023-11-28T05:44:26Z)
• Personalized Federated Learning under Mixture of Distributions [98.25444470990107]
  We propose a novel approach to Personalized Federated Learning (PFL), which utilizes Gaussian mixture models (GMM) to fit the input data distributions across diverse clients. FedGMM possesses an additional advantage of adapting to new clients with minimal overhead, and it also enables uncertainty quantification. Empirical evaluations on synthetic and benchmark datasets demonstrate the superior performance of our method in both PFL classification and novel sample detection.
  arXiv  Detail & Related papers  (2023-05-01T20:04:46Z)
• On the Convergence of Clustered Federated Learning [57.934295064030636]
  In a federated learning system, the clients, e.g. mobile devices and organization participants, usually have different personal preferences or behavior patterns. This paper proposes a novel weighted client-based clustered FL algorithm to leverage the client's group and each client in a unified optimization framework.
  arXiv  Detail & Related papers  (2022-02-13T02:39:19Z)
• Unified Group Fairness on Federated Learning [22.143427873780404]
  Federated learning (FL) has emerged as an important machine learning paradigm where a global model is trained based on private data from distributed clients. Recent researches focus on achieving fairness among clients, but they ignore the fairness towards different groups formed by sensitive attribute(s) (e.g., gender and/or race) We propose a novel FL algorithm, named Group Distributionally Robust Federated Averaging (G-DRFA), which mitigates the distribution shift across groups with theoretical analysis of convergence rate.
  arXiv  Detail & Related papers  (2021-11-09T08:21:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.