## 9 Challenges, Opportunities, Limitations, and Future Trends
Regulatory Compliance: Efforts will be made to align federated learning practices with global regulatory standards, promoting wider adoption in the medical industry by ensuring compliance with data protection laws. 7. Interoperability Standards: The development of interoperability standards will facilitate seamless data integration and model training across diverse biomedical systems and electronic health record (EHR) platforms. 8. Scalable Infrastructure: Investments in scalable and efficient federated learning infrastructure will enable the handling of increasing volumes of health data, making the technology accessible to more providers. 9. Clinical Trial Optimization: FL will be used to optimize clinical trials by securely aggregating data from multiple sites, enhancing patient recruitment, monitoring, and outcome assessment without compromising data privacy. 10. Patient Engagement and Trust: Increased focus on patient engagement and building trust in federated learning systems will be essential, ensuring that patients are informed and confident in their privacy and that their data is being used beneficially. These trends indicate a promising future for federated learning in the biomedical field, with the potential to transform patient care, research, and the overall efficiency of biomedical systems. Likewise, to further enhance the forward-looking perspective of future t trends in federated learning (FL), it is essential to consider emerging technologies like quantum computing. The rise in quantum computing holds significant promise for the future of federated learning, potentially solving the computational bottlenecks associated with secure multi-party computation and homomorphic encryption. Quantum algorithms could drastically reduce the time required for encryption and decryption tasks, allowing for faster and more efficient privacy-preserving operations in FL. This breakthrough could enable the large-scale deployment of FL systems in real-time medical diagnostics, genomic research, and drug discovery, further advancing the precision and speed of biomedical innovations. By integrating these emerging technologies, federated learning could become more robust, scalable, and secure, paving the way for transformative improvements in medical research and patient care. To conclude, the authors of this work emphasize the key concepts, issues, and challenges of integrating DTs with federated learning in smart biomedical systems. Table 6 compares existing research in this domain with the present study across various parameters. This comparison reveals that the present study addresses several aspects not covered in any single existing survey, making this research a significant novelty. The table also highlights the scope of various studies on new technologies and related fields, such as health informatics, edge computing, medical processes, big data, and IoMT security. It focuses on key aspects like security issues, challenges, and future research directions. Most existing studies (Refs. [88; 89; 90; 91; 92]) either neglect security issues, challenges, or DTs, or only partially cover future research directions. In contrast, the present review (published in 2025) comprehensively addresses security issues, challenges, and future research directions, and incorporates DTs, underscoring its unique and thorough approach in the context of FL and healthcare.