Federated Learning for Smart Healthcare: A Survey (2111.08834v1)

Abstract: Recent advances in communication technologies and Internet-of-Medical-Things have transformed smart healthcare enabled by AI. Traditionally, AI techniques require centralized data collection and processing that may be infeasible in realistic healthcare scenarios due to the high scalability of modern healthcare networks and growing data privacy concerns. Federated Learning (FL), as an emerging distributed collaborative AI paradigm, is particularly attractive for smart healthcare, by coordinating multiple clients (e.g., hospitals) to perform AI training without sharing raw data. Accordingly, we provide a comprehensive survey on the use of FL in smart healthcare. First, we present the recent advances in FL, the motivations, and the requirements of using FL in smart healthcare. The recent FL designs for smart healthcare are then discussed, ranging from resource-aware FL, secure and privacy-aware FL to incentive FL and personalized FL. Subsequently, we provide a state-of-the-art review on the emerging applications of FL in key healthcare domains, including health data management, remote health monitoring, medical imaging, and COVID-19 detection. Several recent FL-based smart healthcare projects are analyzed, and the key lessons learned from the survey are also highlighted. Finally, we discuss interesting research challenges and possible directions for future FL research in smart healthcare.

Federated Learning in Smart Healthcare

Federated learning (FL) has emerged as a promising solution for deploying AI in healthcare settings while addressing data privacy concerns. Traditional AI methods often depend on centralized data collection, which can be challenging in a healthcare context due to privacy regulations and the practicalities of accessing vast quantities of distributed data. In this survey, the potential of FL as a decentralized alternative is explored, detailing its applications and the advances supporting its integration into the healthcare sector.

The paper provides a comprehensive review of how FL can facilitate collaborative learning between stakeholders in smart healthcare ecosystems, such as hospitals, clinics, and patient devices. This distributed AI model enables multiple data clients to collaboratively train AI models without sharing sensitive data, crucial in reducing the risk of data exposure and aligning with stringent healthcare regulations like HIPAA.

Advanced FL Designs

The survey introduces several key advancements in FL relevant to healthcare:

• Resource-aware FL: To optimize device scheduling and resource allocation, various models ensure efficient use of network resources while minimizing training latency.
• Secure FL: Implementations with consensus and blockchain technologies are discussed, which enhance security by removing the single point of failure and enabling decentralized management of federated updates.
• Privacy-enhanced FL: Differently private FL approaches, like adding noise to model updates, help protect against potential data breaches and offer privacy guarantees during distributed training.
• Incentive-aware FL: Game-theoretic models and deep reinforcement learning frameworks are deployed to incentivize participation in FL processes, crucial for gathering diverse and robust datasets.
• Personalized FL: Tailored learning models that account for the heterogeneity of data across different clients are explored, enhancing system adaptability and maintaining performance across varied healthcare applications.

Applications in Healthcare

FL has versatile applications in healthcare:

• EHR Management: FL helps manage electronic health records efficiently, enabling predictive analytics for patient management while safeguarding sensitive information.
• Remote Health Monitoring: Personalized services are facilitated through distributed learning models, supporting applications like fall detection and chronic disease monitoring.
• Medical Imaging: FL supports large-scale imaging data analysis, such as in brain MRI and X-ray diagnostics, critical for remote diagnostic collaborations and enhancing AI capabilities across medical institutions.
• COVID-19 Detection: During the COVID-19 pandemic, FL played an essential role in collaborative detection using imaging data, protecting privacy while enabling joint analytical efforts across global institutions.

Challenges and Future Directions

Several challenges in deploying FL in healthcare are discussed, such as communication reliability in federated settings, quality and standardization of datasets, and the integration of FL systems in evolving 5G/6G networks. Addressing these concerns is vital to maximizing the beneficial impacts of FL in healthcare.

The paper posits that continued research should focus on developing comprehensive standards for FL implementations, enhancing communication protocols, and creating robust datasets to improve AI training outcomes. Moreover, exploring more generalizable frameworks to support diverse healthcare applications within FL infrastructures is crucial as these systems become more prevalent.

Conclusion

Federated learning offers a strategic pathway to overcoming numerous challenges faced by AI in healthcare settings. By balancing the need for sophisticated data analytics with stringent data protection requirements, FL stands as a pivotal development in advancing smart healthcare solutions. The paper provides substantial evidence that FL can revolutionize healthcare by facilitating scalable, collaborative, and privacy-preserving AI operations, which are essential for the next generation of healthcare technologies. This set of guidelines and research directions will undoubtedly stimulate further investigation and innovation, paving the way for more robust and integrated healthcare applications.