Enhancing Federated Survival Analysis through Peer-Driven Client Reputation in Healthcare

TL;DR

This paper introduces a peer-driven reputation mechanism for federated survival analysis in healthcare, improving model robustness and privacy by integrating decentralized feedback and differential privacy, leading to more accurate and stable predictions.

Contribution

It presents a novel hybrid reputation system that decouples aggregation from reputation, enhancing federated survival analysis with privacy-preserving peer evaluation and dynamic trust adjustment.

Findings

Achieves higher and more stable C-index values compared to baseline FL methods.

Effectively down-weights noisy or unhelpful client updates.

Demonstrates robustness across synthetic and real-world datasets.

Abstract

Federated Learning (FL) holds great promise for digital health by enabling collaborative model training without compromising patient data privacy. However, heterogeneity across institutions, lack of sustained reputation, and unreliable contributions remain major challenges. In this paper, we propose a robust, peer-driven reputation mechanism for federated healthcare that employs a hybrid communication model to integrate decentralized peer feedback with clustering-based noise handling to enhance model aggregation. Crucially, our approach decouples the federated aggregation and reputation mechanisms by applying differential privacy to client-side model updates before sharing them for peer evaluation. This ensures sensitive information remains protected during reputation computation, while unaltered updates are sent to the server for global model training. Using the Cox Proportional Hazards model for survival analysis across multiple federated nodes, our framework addresses both data heterogeneity and reputation deficit by dynamically adjusting trust scores based on local performance improvements measured via the concordance index. Experimental evaluations on both synthetic datasets and the SEER dataset demonstrate that our method consistently achieves high and stable C-index values, effectively down-weighing noisy client updates and outperforming FL methods that lack a reputation system.