## 9 Challenges, Opportunities, Limitations, and Future Trends
Likewise, some limitations of implementing federated learning (FL) with DTs in the medical field as follows:  
* Computational Complexity: Integrating real-time data from digital models with decentralized FL systems can require significant computational resources, including storage, bandwidth, and processing power, which may be inconsistent across biomedical institutions. * Data Synchronization: Ensuring consistency and the synchronization of real-time data across multiple sources in DTs, while maintaining the integrity of FL models, is challenging and can affect model performance. * Privacy vs. Performance Trade-off: Although FL enhances privacy by keeping data decentralized, the transmission of model updates still carries the risk of information leakage. Privacy-preserving techniques, like differential privacy, may degrade model accuracy. * Scalability Challenges: Scaling FL with models to accommodate large telemedicine networks may increase latency, communication overhead, and resource requirements, making real-time applications more difficult. * Infrastructure Variability: Medical institutions may have differing levels of technological infrastructure, which could limit widespread adoption and integration due to inconsistencies between hardware and connectivity capabilities. These points highlight the key technical and practical challenges that need to be addressed for the successful implementation of FL and DTs in the biomedical field. On the other hand, the integration of blockchain in medical systems also allows benefits such as data integrity, transparency, and security; however, serious limitations exist. Scalability is an important issue, as traditional blockchain networks struggle to keep up with the enormous numbers of transactions generated by big healthcare systems, resulting in delays and paralyzing system efficiency. In addition, the energy consumption of the various consensus mechanisms, mainly PoW, brings into question the sustainability of blockchain solutions, especially in medical systems, and their negative effects on the environment. Also, smart contracts can result in additional vulnerabilities: bugs or coding errors may lead to unforeseen consequences and data breaches that malevolent individuals can take advantage of. These limitations should, therefore, be tackled to ensure that blockchain applications in healthcare are scalable, energy-efficient, secure, and transparent, in order to be able to counter rapidly emerging cyber threats. Ultimately, FL and blockchain are poised to revolutionize the biomedical industry by enabling secure and collaborative data sharing across institutions without compromising patient privacy. Some anticipated future trends are listed as follows:  
1. Enhanced Data Privacy and Security: Continued advancements in federated learning algorithms will focus on strengthening data privacy and security, ensuring patient information remains confidential while facilitating large-scale medical research and repelling any fraudulent behavior or attacks [86; 87]. 2. Integration with IoT and Edge Devices: The integration of FL with Internet of Things (IoT) devices and edge computing will enable real-time health monitoring and analysis, providing immediate insights and interventions without the need for data to leave the device. 3. Personalized Medicine: FL will support the development of personalized medicine by allowing researchers to train models on diverse datasets from different populations, leading to more accurate and individualized treatment plans. 4. Collaborative Research Networks: The establishment of extensive collaborative networks across hospitals, research institutions, and pharmaceutical companies will drive innovation and accelerate the discovery of new treatments and diagnostic tools. 5. Improved Model Robustness: Techniques to improve the robustness and generalizability of federated learning models will be developed, ensuring they perform well across varied care settings and patient demographics. 6.