Multi-dimensional Fair Federated Learning
Cong Su Guoxian Yu Jun Wang Hui Li Qingzhong Li Han Yu
Federated learning (FL) has emerged as a promising collaborative and secure
paradigm for training a model from decentralized data without compromising
privacy. Group fairness and client fairness are two dimensions of fairness that
are important for FL. Standard FL can result in disproportionate disadvantages
for certain clients, and it still faces the challenge of treating different
groups equitably in a population. The problem of privately training fair FL
models without compromising the generalization capability of disadvantaged
clients remains open. In this paper, we propose a method, called mFairFL, to
address this problem and achieve group fairness and client fairness
simultaneously. mFairFL leverages differential multipliers to construct an
optimization objective for empirical risk minimization with fairness
constraints. Before aggregating locally trained models, it first detects
conflicts among their gradients, and then iteratively curates the direction and
magnitude of gradients to mitigate these conflicts. Theoretical analysis proves
mFairFL facilitates the fairness in model development. The experimental
evaluations based on three benchmark datasets show significant advantages of
mFairFL compared to seven state-of-the-art baselines.