Fair Federated Learning via Bounded Group Loss
Fair prediction across protected groups is an important constraint for many federated learning applications. However, prior work studying group fair federated learning lacks formal convergence or fairness guarantees. In this work we propose a general framework for provably fair federated learning. In particular, we explore and extend the notion of Bounded Group Loss as a theoretically-grounded approach for group fairness. Using this setup, we propose a scalable federated optimization method that optimizes the empirical risk under a number of group fairness constraints. We provide convergence guarantees for the method as well as fairness guarantees for the resulting solution. Empirically, we evaluate our method across common benchmarks from fair ML and federated learning, showing that it can provide both fairer and more accurate predictions than baseline approaches.
Code (0)
등록된 구현이 없습니다.
Tasks
FairnessFederated LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Unified Group Fairness on Federated Learning
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 t…
AttributeFairnessFederated LearningFederated Learning with Relative Fairness
This paper proposes a federated learning framework designed to achieve \textit{relative fairness} for clients. Traditional federated learning frameworks typically ensure absolute fairness by guaranteeing minimum performa…
Computational EfficiencyFairnessFederated LearningMitigating Group Bias in Federated Learning: Beyond Local Fairness
The issue of group fairness in machine learning models, where certain sub-populations or groups are favored over others, has been recognized for some time. While many mitigation strategies have been proposed in centraliz…
FairnessFederated LearningMinimax Demographic Group Fairness in Federated Learning
Federated learning is an increasingly popular paradigm that enables a large number of entities to collaboratively learn better models. In this work, we study minimax group fairness in federated learning scenarios where d…
FairnessFederated LearningPost-Fair Federated Learning: Achieving Group and Community Fairness in Federated Learning via Post-processing
Federated Learning (FL) is a distributed machine learning framework in which a set of local communities collaboratively learn a shared global model while retaining all training data locally within each community. Two not…
FairnessFederated Learning