Federated learning for IoMT-enabled predictive healthcare and remote patient monitoring: a systematic review

Background The rapid growth of the Internet of Medical Things (IoMT) has enabled continuous monitoring of physiological signals and data-driven healthcare services, supporting predictive analytics, personalized treatment, and remote patient monitoring. However, conventional centralized machine learning approaches require sensitive patient data to be aggregated on centralized servers, raising concerns regarding privacy, security, regulatory compliance, scalability, and communication overhead. This systematic review critically evaluates federated learning (FL) approaches for IoMT-enabled predictive healthcare and remote patient monitoring, focusing on FL architectures, aggregation strategies, privacy-preserving mechanisms, communication efficiency, scalability, and practical deployment challenges. Methods This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. Relevant literature was collected from IEEE Xplore, ScienceDirect, SpringerLink, ACM Digital Library, PubMed, Scopus, and Web of Science. Following screening and eligibility assessment, 69 studies published between 2020 and 2026 were included. The selected studies were evaluated using a structured quality assessment framework covering dataset validity, experimental rigor, reproducibility, scalability evaluation, privacy protection, and reporting quality. The evidence was synthesized to compare FL architectures, aggregation techniques, security mechanisms, communication performance, and healthcare applications. Results The findings indicate that FL enables collaborative model training without directly sharing raw patient data. Hierarchical and edge-assisted FL architectures can reduce communication overhead and improve scalability in distributed healthcare environments. Differential privacy, secure aggregation, homomorphic encryption, secure multiparty computation, and blockchain integration were identified as privacy-preserving mechanisms used in the reviewed studies. Many studies reported predictive accuracies exceeding 90%, while communication cost reductions of up to 45% were reported for certain hierarchical FL deployments. However, challenges involving non-independent and identically distributed (non-IID) data, device heterogeneity, limited computational resources, real-time adaptability, and adversarial robustness remain unresolved. Conclusion Federated learning represents a promising approach to secure, scalable, and privacy-preserving healthcare analytics in IoMT ecosystems. Future research should prioritize adaptive federated architectures, lightweight privacy mechanisms, personalized federated learning, robust defenses against adversarial attacks, and large-scale real-world validation to support clinical adoption and improve healthcare outcomes.