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

2025-05-22

AI Summary

Medical federated survival analysis faces critical challenges including strong inter-institutional data heterogeneity, unreliable client contributions, and the absence of long-term, auditable reputation mechanisms. Method: We propose a decentralized, privacy-preserving peer-based reputation mechanism. Our approach introduces a novel decoupled framework that separates reputation evaluation from model aggregation; incorporates differential privacy–protected assessment of client update quality; dynamically adjusts trust weights using an improved C-index–based utility metric; and enhances robustness via clustering-based noise filtering and peer feedback. Results: Experiments on the SEER dataset and synthetic survival data demonstrate that our method effectively suppresses interference from noisy or malicious clients. It achieves superior C-index stability and mean performance compared to baseline federated methods without reputation mechanisms, enabling high-accuracy, robust, and auditable federated survival modeling.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Search and Optimization: Distributed Search

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Security and Privacy: Data transparency and provenance
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Addressing data heterogeneity in federated healthcare learning
• Improving client reputation for reliable model contributions
• Ensuring privacy in peer-driven reputation mechanisms

Innovation

• Peer-driven reputation mechanism for federated healthcare
• Hybrid communication model with noise handling
• Differential privacy for secure client updates