F3: Fair and Federated Face Attribute Classification with Heterogeneous Data
Fairness across different demographic groups is an essential criterion for face-related tasks, Face Attribute Classification (FAC) being a prominent example. Apart from this trend, Federated Learning (FL) is increasingly gaining traction as a scalable paradigm for distributed training. Existing FL approaches require data homogeneity to ensure fairness. However, this assumption is too restrictive in real-world settings. We propose F3, a novel FL framework for fair FAC under data heterogeneity. F3 adopts multiple heuristics to improve fairness across different demographic groups without requiring data homogeneity assumption. We demonstrate the efficacy of F3 by reporting empirically observed fairness measures and accuracy guarantees on popular face datasets. Our results suggest that F3 strikes a practical balance between accuracy and fairness for FAC.
Code (1)
Tasks
AttributeClassificationFairnessFederated LearningSimilar Papers 제목 키워드 기반
pFedFair: Towards Optimal Group Fairness-Accuracy Trade-off in Heterogeneous Federated Learning
Federated learning (FL) algorithms commonly aim to maximize clients' accuracy by training a model on their collective data. However, in several FL applications, the model's decisions should meet a group fairness constrai…
FairnessFederated LearningPersonalized Federated LearningFeDa4Fair: Client-Level Federated Datasets for Fairness Evaluation
Federated Learning (FL) enables collaborative model training across multiple clients without sharing clients' private data. However, fairness remains a key concern, as biases in local clients' datasets can impact the ent…
AttributeBenchmarkingFairnessFederated LearningFederated Learning at the Forefront of Fairness: A Multifaceted Perspective
Fairness in Federated Learning (FL) is emerging as a critical factor driven by heterogeneous clients' constraints and balanced model performance across various scenarios. In this survey, we delineate a comprehensive clas…
Federated LearningFairFedMed: Benchmarking Group Fairness in Federated Medical Imaging with FairLoRA
Fairness remains a critical concern in healthcare, where unequal access to services and treatment outcomes can adversely affect patient health. While Federated Learning (FL) presents a collaborative and privacy-preservin…
Medical Image ClassificationFederated LearningPrivacy-Preserving Federated Learning with Verifiable Fairness Guarantees
Federated learning enables collaborative model training across distributed institutions without centralizing sensitive data; however, ensuring algorithmic fairness across heterogeneous data distributions while preserving…
Federated Learning