Novel Federated Graph Contrastive Learning for IoMT Security: Protecting Data Poisoning and Inference Attacks

Amarudin Daulay, Kalamullah Ramli, Ruki Harwahyu (), Taufik Hidayat and Bernardi Pranggono ()
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Amarudin Daulay: Department of Electrical Engineering, Universitas Indonesia, Depok 16424, Indonesia
Kalamullah Ramli: Department of Electrical Engineering, Universitas Indonesia, Depok 16424, Indonesia
Ruki Harwahyu: Department of Electrical Engineering, Universitas Indonesia, Depok 16424, Indonesia
Taufik Hidayat: Department of Electrical Engineering, Universitas Indonesia, Depok 16424, Indonesia
Bernardi Pranggono: School of Computing and Information Science, Anglia Ruskin University, Cambridge CB1 1PT, UK

Mathematics, 2025, vol. 13, issue 15, 1-22

Abstract: Malware evolution presents growing security threats for resource-constrained Internet of Medical Things (IoMT) devices. Conventional federated learning (FL) often suffers from slow convergence, high communication overhead, and fairness issues in dynamic IoMT environments. In this paper, we propose FedGCL, a secure and efficient FL framework integrating contrastive graph representation learning for enhanced feature discrimination, a Jain-index-based fairness-aware aggregation mechanism, an adaptive synchronization scheduler to optimize communication rounds, and secure aggregation via homomorphic encryption within a Trusted Execution Environment. We evaluate FedGCL on four benchmark malware datasets (Drebin, Malgenome, Kronodroid, and TUANDROMD) using 5 to 15 graph neural network clients over 20 communication rounds. Our experiments demonstrate that FedGCL achieves 96.3% global accuracy within three rounds and converges to 98.9% by round twenty—reducing required training rounds by 45% compared to FedAvg—while incurring only approximately 10% additional computational overhead. By preserving patient data privacy at the edge, FedGCL enhances system resilience without sacrificing model performance. These results indicate FedGCL’s promise as a secure, efficient, and fair federated malware detection solution for IoMT ecosystems.

Keywords: federated learning; graph representation learning; IoMT; malware (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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