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Federated learning (FL) is a promising technique for decentralized privacy-preserving Machine Learning (ML) with a diverse pool of participating devices with varying device capabilities. However, existing approaches to handle such heterogeneous environments do not consider “fairness” in model aggregation, resulting in significant performance variation among devices. Meanwhile, prior works on FL fairness remain hardware-oblivious and cannot be applied directly without severe performance penalties. To address this issue, we propose a novel hardware-sensitive FL method called\(\mathsf {FairHetero}\)that promotes fairness among heterogeneous federated clients. Our approach offers tunable fairness within a group of devices with the same ML architecture as well as across different groups with heterogeneous models. Our evaluation underMNIST,FEMNIST,CIFAR10, andSHAKESPEAREdatasets demonstrates that\(\mathsf {FairHetero}\)can reduce variance among participating clients’ test loss compared to the existing state-of-the-art techniques, resulting in increased overall performance.
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This content will become publicly available on April 11, 2026
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.
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- Award ID(s):
- 2238743
- PAR ID:
- 10593564
- Publisher / Repository:
- Association for the Advancement of Artificial Intelligence (AAAI) 2025
- Date Published:
- Journal Name:
- Proceedings of the AAAI Conference on Artificial Intelligence
- Volume:
- 39
- Issue:
- 19
- ISSN:
- 2159-5399
- Page Range / eLocation ID:
- 20104 to 20112
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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