Mitigating Group Bias in Federated Learning for Heterogeneous Devices

Federated Learning is emerging as a privacy-preserving model training approach in distributed edge applications. As such, most edge deployments are heterogeneous in nature i.e., their sensing capabilities and environments vary across deployments. This edge heterogeneity violates the independence and identical distribution (IID) property of local data across clients and produces biased global models i.e. models that contribute to unfair decision-making and discrimination against a particular community or a group. Existing bias mitigation techniques only focus on bias generated from label heterogeneity in non-IID data without accounting for domain variations due to feature heterogeneity and do not address global group-fairness property. Our work proposes a group-fair FL framework that minimizes group-bias while preserving privacy and without resource utilization overhead. Our main idea is to leverage average conditional probabilities to compute a cross-domain group \textit{importance weights} derived from heterogeneous training data to optimize the performance of the worst-performing group using a modified multiplicative weights update method. Additionally, we propose regularization techniques to minimize the difference between the worst and best-performing groups while making sure through our thresholding mechanism to strike a balance between bias reduction and group performance degradation. Our evaluation of human emotion recognition and image classification benchmarks assesses the fair decision-making of our framework in real-world heterogeneous settings.