A Resilient Hybrid Mechanism for Protecting Healthcare Data from Model Poisoning in Federated Learning
Year: 2025
Type: conference-paper
Abstract: Federated learning has emerged as a promising paradigm for collaborative machine learning in healthcare, enabling medical institutions to jointly train models while preserving patient data privacy and complying with regulations like HIPAA. However, its decentralized nature makes it vulnerable to model poisoning attacks, where malicious participants can compromise the global model’s performance, potentially leading to incorrect diagnoses or treatment recommendations. In this paper, we propose a novel hybrid defense mechanism that combines statistical anomaly detection with random partial aggregation to protect against both obvious and subtle poisoning attacks in healthcare FL systems. Our approach first employs a robust anomaly detection system to identify and filter out clearly malicious updates, then applies a random partial aggregation strategy to minimize the impact of subtle poisoning attempts that evade detection. Unlike existing methods that focus solely on either detection or robust aggregation, our hybrid approach provides two layers of defense working in a complementary fashion. Experimental results on the MNIST dataset, serving as a proof of concept that can be extended to medical imaging and clinical data, demonstrate that our method maintains model accuracy while significantly reducing the impact of poisoning attacks compared to the traditional FedAvg approach. Our approach successfully detects obvious attacks through anomaly detection while mitigating subtle poisoning through random aggregation, achieving a better balance between model performance and security. The proposed method is computationally efficient and can be readily integrated into existing healthcare FL systems, offering a robust solution for secure collaborative learning in medical environments where model reliability and patient safety are paramount.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7160266595