Add to ORKGRecently, lots of algorithms have been proposed for learning a fair classifier from decentralized data. However, many theoretical and algorithmic questions remain open. First, is federated learning necessary, i.e., can we simply train locally fair classifiers and aggregate them? In this work, we first propose a new theoretical framework, with which we demonstrate that federated learning can strictly boost model fairness compared with such non-federated algorithms. We then theoretically and empirically show that the performance tradeoff of FedAvg-based fair learning algorithms is strictly worse than that of a fair classifier trained on centralized data. To bridge this gap, we propose FedFB, a private fair learning algorithm on decentralized ...
Fairness has been considered as a critical problem in federated learning (FL). In this work, we anal...
Federated learning aims to collaboratively train models without accessing their client's local priva...
We show that participating in federated learning can be detrimental to group fairness. In fact, the ...
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 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...
Federated Learning (FL) enables data owners to train a shared global model without sharing their pri...
Federated learning has emerged as a promising paradigm for privacy-preserving collaboration among di...
Federated learning is an emerging decentralized machine learning scheme that allows multiple data ow...
Vertical federated learning (VFL) is a privacy-preserving machine learning paradigm that can learn m...
In many real-world situations, data is distributed across multiple self-interested agents. These age...
With the increasingly broad deployment of Federated Learning (FL) systems in the real world, it is c...
Federated learning provides an effective paradigm to jointly optimize a model benefited from rich di...
The issue of potential privacy leakage during centralized AI's model training has drawn intensive co...
Fairness has been considered as a critical problem in federated learning (FL). In this work, we anal...
Federated learning aims to collaboratively train models without accessing their client's local priva...
We show that participating in federated learning can be detrimental to group fairness. In fact, the ...
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 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...
Federated Learning (FL) enables data owners to train a shared global model without sharing their pri...
Federated learning has emerged as a promising paradigm for privacy-preserving collaboration among di...
Federated learning is an emerging decentralized machine learning scheme that allows multiple data ow...
Vertical federated learning (VFL) is a privacy-preserving machine learning paradigm that can learn m...
In many real-world situations, data is distributed across multiple self-interested agents. These age...
With the increasingly broad deployment of Federated Learning (FL) systems in the real world, it is c...
Federated learning provides an effective paradigm to jointly optimize a model benefited from rich di...
The issue of potential privacy leakage during centralized AI's model training has drawn intensive co...
Fairness has been considered as a critical problem in federated learning (FL). In this work, we anal...
Federated learning aims to collaboratively train models without accessing their client's local priva...
We show that participating in federated learning can be detrimental to group fairness. In fact, the ...