Federated learning (FL) is a distributed machine learning framework that enables multiple clients to collaboratively train models without raw data exchange. Prior studies on FL mainly focus on optimizing learning performance, enhancing privacy preservation, and improving attack resilience. However, little work studies how to mitigate the unfairness of federated trained models while unfair models would make discriminatory decisions toward certain groups or populations (e.g., favoring males over females), leading to serious ethical concerns. Thus, it is crucial to mitigate model unfairness in FL, yet challenging as this requires centralized access to each data point's fairness-sensitive information (e.g., race, gender), which is prohibited in FL. In this work, we propose a novel fair FL framework FedUFA, where the server can aggregate clients’ learned knowledge in an unbiased manner, to obtain fair and high-usability federated trained models. Specifically, to unearth the bias in clients’ local data and account for potentially heterogeneous local models, we propose a knowledge distillation-based FL scheme, where clients’ knowledge of learned features on a public dataset is amalgamated to the server for aggregation. We train an unbiased feature mapper at the server to remove fairness-sensitive latent features and extract fair representations from clients’ submitted raw features. In particular, we design an adversarial training method to train the mapper, which involves apredictoraiming to maximize the prediction accuracy on the FL task and adiscriminatorintending to help identify fairness-sensitive features. Extensive experiments on real-world datasets demonstrate the effectiveness of FedUFA.

Heterogeneous model aggregation (HMA) is an effective paradigm that integrates on-device trained models heterogeneous in architecture and target task into a comprehensive model. Recent works adopt knowledge distillation to amalgamate the knowledge of learned features and predictions from heterogeneous on-device models to realize HMA. However, most of them ignore that the disclosure of learned features exposes on-device models to privacy attacks. Moreover, the aggregated model may suffer from the imbalanced supervision caused by the uneven distribution of amalgamated knowledge about each class and show class bias. In this article, to address these issues, we propose a response-based class-balanced heterogeneous model aggregation mechanism, called CBHMA. It can effectively achieve HMA in a privacy-preserving manner and alleviate class bias in the aggregated model. Specifically, CBHMA aggregates on-device models by using only their response information to reduce their privacy leakage risk. To mitigate the impact of imbalanced supervision, CBHMA quantitatively measures the imbalanced supervision level for each class. Based on that, CBHMA customizes fine-grained misclassification costs for each class and utilizes such costs to adjust the importance of each class (more importance to classes with weaker supervision) in the response-based HMA algorithm. Extensive experiments on two real-world datasets demonstrate the effectiveness of CBHMA.