Communication-Efficient Federated Risk Difference Estimation for Time-to-Event Clinical Outcomes

2026-01-21

AI Summary

This study addresses the challenge of absolute survival risk estimation in multi-center medical research, where privacy constraints and reliance on a central server hinder clinically interpretable inference. Existing federated learning approaches typically yield only relative effect measures and lack interpretability for absolute risk. To overcome these limitations, we propose FedRD, a novel server-agnostic and communication-efficient federated framework for estimating risk differences. FedRD requires only one round of communication under stratified settings or three rounds in non-stratified scenarios, enabling confidence interval construction and hypothesis testing directly from distributed survival data. Theoretically, the non-stratified variant is shown to be asymptotically equivalent to centralized analysis. Extensive experiments on both simulated and real-world multinational datasets demonstrate that FedRD substantially outperforms local analyses and existing federated baselines, delivering privacy-preserving, interpretable, and statistically inferable absolute risk estimates.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Natural Language Processing: Safety and Robustness

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Large-scale security measurements

Abstract

Privacy-preserving model co-training in medical research is often hindered by server-dependent architectures incompatible with protected hospital data systems and by the predominant focus on relative effect measures (hazard ratios) which lack clinical interpretability for absolute survival risk assessment. We propose FedRD, a communication-efficient framework for federated risk difference estimation in distributed survival data. Unlike typical federated learning frameworks (e.g., FedAvg) that require persistent server connections and extensive iterative communication, FedRD is server-independent with minimal communication: one round of summary statistics exchange for the stratified model and three rounds for the unstratified model. Crucially, FedRD provides valid confidence intervals and hypothesis testing--capabilities absent in FedAvg-based frameworks. We provide theoretical guarantees by establishing the asymptotic properties of FedRD and prove that FedRD (unstratified) is asymptotically equivalent to pooled individual-level analysis. Simulation studies and real-world clinical applications across different countries demonstrate that FedRD outperforms local and federated baselines in both estimation accuracy and prediction performance, providing an architecturally feasible solution for absolute risk assessment in privacy-restricted, multi-site clinical studies.

Problem

• federated learning
• risk difference
• survival analysis
• privacy-preserving
• absolute risk

Innovation

• Federated Learning
• Risk Difference
• Survival Analysis
• Communication Efficiency
• Privacy-Preserving