Fair Federated Survival Analysis

Federated Survival Analysis (FSA) is an emerging Federated Learning (FL) paradigm that enables training survival models on decentralized data while preserving privacy. However, existing FSA approaches largely overlook the potential risk of bias in predictions arising from demographic and censoring disparities across clients' datasets, which impacts the fairness and performance of federated survival models, especially for underrepresented groups. To address this gap, we introduce FairFSA, a novel FSA framework that adapts existing fair survival models to the federated setting. FairFSA jointly trains survival models using distributionally robust optimization, penalizing worst-case errors across subpopulations that exceed a specified probability threshold. Partially observed survival outcomes in clients are reconstructed with federated pseudo values (FPV) before model training to address censoring. Furthermore, we design a weight aggregation strategy by enhancing the FedAvg algorithm with a fairness-aware concordance index-based aggregation method to foster equitable performance distribution across clients. To the best of our knowledge, this is the first work to study and integrate fairness into Federated Survival Analysis. Comprehensive experiments on distributed non-IID datasets demonstrate FairFSA's superiority in fairness and accuracy over state-of-the-art FSA methods, establishing it as a robust FSA approach capable of handling censoring while providing equitable and accurate survival predictions for all subjects.