Unified Group Fairness on Federated Learning

• URL: http://arxiv.org/abs/2111.04986v1
• Date: Tue, 9 Nov 2021 08:21:38 GMT
• Title: Unified Group Fairness on Federated Learning
• Authors: Fengda Zhang, Kun Kuang, Yuxuan Liu, Chao Wu, Fei Wu, Jiaxun Lu, Yunfeng Shao, Jun Xiao
• Abstract summary: 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.
• Score: 22.143427873780404
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning (FL) has emerged as an important machine learning paradigm where a global model is trained based on the private data from distributed clients. However, most of existing FL algorithms cannot guarantee the performance fairness towards different clients or different groups of samples because of the distribution shift. 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), which is important and practical in real applications. To bridge this gap, we formulate the goal of unified group fairness on FL which is to learn a fair global model with similar performance on different groups. To achieve the unified group fairness for arbitrary sensitive attribute(s), 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. Specifically, we treat the performance of the federated global model at each group as an objective and employ the distributionally robust techniques to maximize the performance of the worst-performing group over an uncertainty set by group reweighting. We validate the advantages of the G-DRFA algorithm with various kinds of distribution shift settings in experiments, and the results show that G-DRFA algorithm outperforms the existing fair federated learning algorithms on unified group fairness.

Related papers

• Enhancing Group Fairness in Federated Learning through Personalization [15.367801388932145]
  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.
  arXiv  Detail & Related papers  (2024-07-27T19:55:18Z)
• Federated Fairness without Access to Sensitive Groups [12.888927461513472]
  Current approaches to group fairness in federated learning assume the existence of predefined and labeled sensitive groups during training. We propose a new approach to guarantee group fairness that does not rely on any predefined definition of sensitive groups or additional labels.
  arXiv  Detail & Related papers  (2024-02-22T19:24:59Z)
• Dynamic Fair Federated Learning Based on Reinforcement Learning [19.033986978896074]
  Federated learning enables a collaborative training and optimization of global models among a group of devices without sharing local data samples. We propose a dynamic q fairness federated learning algorithm with reinforcement learning, called DQFFL. Our DQFFL outperforms the state-of-the-art methods in terms of overall performance, fairness and convergence speed.
  arXiv  Detail & Related papers  (2023-11-02T03:05:40Z)
• Chasing Fairness Under Distribution Shift: A Model Weight Perturbation Approach [72.19525160912943]
  We first theoretically demonstrate the inherent connection between distribution shift, data perturbation, and model weight perturbation. We then analyze the sufficient conditions to guarantee fairness for the target dataset. Motivated by these sufficient conditions, we propose robust fairness regularization (RFR)
  arXiv  Detail & Related papers  (2023-03-06T17:19:23Z)
• Re-weighting Based Group Fairness Regularization via Classwise Robust Optimization [30.089819400033985]
  We propose a principled method, dubbed as ours, which unifies the two learning schemes by incorporating a well-justified group fairness metric into the training objective. We develop an iterative optimization algorithm that minimizes the resulting objective by automatically producing the correct re-weights for each group. Our experiments show that FairDRO is scalable and easily adaptable to diverse applications.
  arXiv  Detail & Related papers  (2023-03-01T12:00:37Z)
• Learning Informative Representation for Fairness-aware Multivariate Time-series Forecasting: A Group-based Perspective [50.093280002375984]
  Performance unfairness among variables widely exists in multivariate time series (MTS) forecasting models. We propose a novel framework, named FairFor, for fairness-aware MTS forecasting.
  arXiv  Detail & Related papers  (2023-01-27T04:54:12Z)
• Beyond ADMM: A Unified Client-variance-reduced Adaptive Federated Learning Framework [82.36466358313025]
  We propose a primal-dual FL algorithm, termed FedVRA, that allows one to adaptively control the variance-reduction level and biasness of the global model. Experiments based on (semi-supervised) image classification tasks demonstrate superiority of FedVRA over the existing schemes.
  arXiv  Detail & Related papers  (2022-12-03T03:27:51Z)
• Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction [76.26710990597498]
  We show that the class-imbalance of the grouped data from randomly selected clients can lead to significant performance degradation. Based on our key observation, we design an efficient client sampling mechanism, i.e., Federated Class-balanced Sampling (Fed-CBS) In particular, we propose a measure of class-imbalance and then employ homomorphic encryption to derive this measure in a privacy-preserving way.
  arXiv  Detail & Related papers  (2022-09-30T05:42:56Z)
• Heterogeneous Federated Learning via Grouped Sequential-to-Parallel Training [60.892342868936865]
  Federated learning (FL) is a rapidly growing privacy-preserving collaborative machine learning paradigm. We propose a data heterogeneous-robust FL approach, FedGSP, to address this challenge. We show that FedGSP improves the accuracy by 3.7% on average compared with seven state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-01-31T03:15:28Z)
• Federating for Learning Group Fair Models [19.99325961328706]
  Federated learning is an increasingly popular paradigm that enables a large number of entities to collaboratively learn better models. We study minmax group fairness in paradigms where different participating entities may only have access to a subset of the population groups during the training phase.
  arXiv  Detail & Related papers  (2021-10-05T12:42:43Z)
• MultiFair: Multi-Group Fairness in Machine Learning [52.24956510371455]
  We study multi-group fairness in machine learning (MultiFair) We propose a generic end-to-end algorithmic framework to solve it. Our proposed framework is generalizable to many different settings.
  arXiv  Detail & Related papers  (2021-05-24T02:30:22Z)

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.