Add to ORKGFederated learning (FL) has garnered considerable attention due to its privacy-preserving feature. Nonetheless, the lack of freedom in managing user data can lead to group fairness issues, where models might be biased towards sensitive factors such as race or gender, even if they are trained using a legally compliant process. To redress this concern, this paper proposes a novel FL algorithm designed explicitly to address group fairness issues. We show empirically on CelebA and ImSitu datasets that the proposed method can improve fairness both quantitatively and qualitatively with minimal loss in accuracy in the presence of statistical heterogeneity and with different numbers of clients. Besides improving fairness, the proposed FL algorithm ...
Vertical federated learning (VFL) is a privacy-preserving machine learning paradigm that can learn m...
International audienceDespite the great potential offered by Artificial Intelligence in the context ...
Federated learning enables a collaborative training and optimization of global models among a group ...
Federated learning (FL) has garnered considerable attention due to its privacy-preserving feature. N...
Training ML models which are fair across different demographic groups is of critical importance due ...
Recent advances in Federated Learning (FL) have brought large-scale collaborative machine learning o...
Federated learning has emerged as a promising paradigm for privacy-preserving collaboration among di...
Recently, lots of algorithms have been proposed for learning a fair classifier from decentralized da...
International audienceFederated learning (FL) is a framework for training machine learning models in...
Federated learning (FL) is a framework for training machine learning models in a distributed and col...
The issue of potential privacy leakage during centralized AI's model training has drawn intensive co...
Federated learning (FL) is a distributed machine learning approach that enables remote devices i.e. ...
Advanced adversarial attacks such as membership inference and model memorization can make federated ...
Repeated parameter sharing in federated learning causes significant information leakage about privat...
Federated Learning (FL) enables data owners to train a shared global model without sharing their pri...
Vertical federated learning (VFL) is a privacy-preserving machine learning paradigm that can learn m...
International audienceDespite the great potential offered by Artificial Intelligence in the context ...
Federated learning enables a collaborative training and optimization of global models among a group ...
Federated learning (FL) has garnered considerable attention due to its privacy-preserving feature. N...
Training ML models which are fair across different demographic groups is of critical importance due ...
Recent advances in Federated Learning (FL) have brought large-scale collaborative machine learning o...
Federated learning has emerged as a promising paradigm for privacy-preserving collaboration among di...
Recently, lots of algorithms have been proposed for learning a fair classifier from decentralized da...
International audienceFederated learning (FL) is a framework for training machine learning models in...
Federated learning (FL) is a framework for training machine learning models in a distributed and col...
The issue of potential privacy leakage during centralized AI's model training has drawn intensive co...
Federated learning (FL) is a distributed machine learning approach that enables remote devices i.e. ...
Advanced adversarial attacks such as membership inference and model memorization can make federated ...
Repeated parameter sharing in federated learning causes significant information leakage about privat...
Federated Learning (FL) enables data owners to train a shared global model without sharing their pri...
Vertical federated learning (VFL) is a privacy-preserving machine learning paradigm that can learn m...
International audienceDespite the great potential offered by Artificial Intelligence in the context ...
Federated learning enables a collaborative training and optimization of global models among a group ...