FedAgain: A Trust-Based and Robust Federated Learning Strategy for an Automated Kidney Stone Identification in Ureteroscopy

2026-03-19

AI Summary

This work addresses the limited robustness of medical imaging AI in identifying kidney stones under multi-center, heterogeneous imaging devices, and corrupted image conditions. To tackle this challenge, the authors propose a federated learning approach based on a dual-trust mechanism that dynamically adjusts client contribution weights by jointly considering local model reliability and divergence from the global model. This strategy effectively suppresses the adverse impact of noisy or anomalous updates while preserving data privacy. Extensive experiments across five datasets—including two private multi-institutional kidney stone collections—demonstrate that the proposed method significantly outperforms standard federated learning, achieving higher diagnostic accuracy and improved model stability under non-IID and noisy settings.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Computer Vision: Adversarial Attacks & Robustness
• Reasoning under Uncertainty: Stochastic Optimization

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
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing

Abstract

The reliability of artificial intelligence (AI) in medical imaging critically depends on its robustness to heterogeneous and corrupted images acquired with diverse devices across different hospitals which is highly challenging. Therefore, this paper introduces FedAgain, a trust-based Federated Learning (Federated Learning) strategy designed to enhance robustness and generalization for automated kidney stone identification from endoscopic images. FedAgain integrates a dual trust mechanism that combines benchmark reliability and model divergence to dynamically weight client contributions, mitigating the impact of noisy or adversarial updates during aggregation. The framework enables the training of collaborative models across multiple institutions while preserving data privacy and promoting stable convergence under real-world conditions. Extensive experiments across five datasets, including two canonical benchmarks (MNIST and CIFAR-10), two private multi-institutional kidney stone datasets, and one public dataset (MyStone), demonstrate that FedAgain consistently outperforms standard Federated Learning baselines under non-identically and independently distributed (non-IID) data and corrupted-client scenarios. By maintaining diagnostic accuracy and performance stability under varying conditions, FedAgain represents a practical advance toward reliable, privacy-preserving, and clinically deployable federated AI for medical imaging.

Problem

• federated learning
• kidney stone identification
• medical imaging
• robustness
• non-IID data

Innovation

• trust-based federated learning
• dual trust mechanism
• robust medical AI
• non-IID data
• kidney stone identification