Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey (2203.09702v1)

Abstract: Recent advances in electronic devices and communication infrastructure have revolutionized the traditional healthcare system into a smart healthcare system by using IoMT devices. However, due to the centralized training approach of AI, the use of mobile and wearable IoMT devices raises privacy concerns with respect to the information that has been communicated between hospitals and end users. The information conveyed by the IoMT devices is highly confidential and can be exposed to adversaries. In this regard, federated learning (FL), a distributive AI paradigm has opened up new opportunities for privacy-preservation in IoMT without accessing the confidential data of the participants. Further, FL provides privacy to end users as only gradients are shared during training. For these specific properties of FL, in this paper we present privacy related issues in IoMT. Afterwards, we present the role of FL in IoMT networks for privacy preservation and introduce some advanced FL architectures incorporating deep reinforcement learning (DRL), digital twin, and generative adversarial networks (GANs) for detecting privacy threats. Subsequently, we present some practical opportunities of FL in smart healthcare systems. At the end, we conclude this survey by providing open research challenges for FL that can be used in future smart healthcare systems

Overview of Federated Learning for Privacy Preservation in Smart Healthcare Systems

The surveyed paper provides a comprehensive examination of federated learning (FL) as a privacy-preserving alternative to centralized AI models in smart healthcare systems, particularly focusing on Internet of Medical Things (IoMT). The authors—Mansoor Ali, Faisal Naeem, Muhammad Tariq, and Georges Kaddoum—address the inherent privacy concerns presented by IoMT devices, which generate and communicate sensitive patient data. In conventional AI models, this data is typically centralized, posing significant privacy risks. FL emerges as a distributed solution where only model gradients are exchanged instead of raw data, mitigating privacy exposure.

Key Contributions

The paper systematically explores privacy-related issues in IoMT and articulates how FL addresses these challenges. Technical and empirical insights are provided by contrasting FL with traditional, centralized AI approaches, especially in the context of handling high-volume healthcare data transmission securely. The authors highlight advanced architectures integrating deep reinforcement learning (DRL), digital twins, and generative adversarial networks (GANs) as tools to enhance privacy measures within FL frameworks.

Comparison with Existing Literature:

• This work distinguishes itself by thoroughly examining privacy issues within healthcare frameworks, surpassing earlier works that either overlooked privacy or focused primarily on industrial IoT applications.
• The survey underscores the innovative applications of FL coupled with AI techniques like GANs and DRL, strategically addressing privacy threats and leveraging data from heterogeneous healthcare environments.

Practical Applications and Implications

The applications of FL in smart healthcare systems are multifaceted. For example, the paper discusses FL's role in efficiently managing electronic health records (EHR) without compromising patient privacy. It further addresses federated learning's potential for medical imaging analytics, including secure collaboration across institutions without data sharing. Specific scenarios like brain tumor imaging and COVID-19 detection exemplify FL's robustness in resolving privacy concerns and data scarcity through adaptive collaborative models.

Future Directions and Challenges

The exploration of FL within IoMT systems unveils several future research trajectories. Addressing communication network issues for optimized resource scheduling in FL applications is vital, given the dynamic nature of healthcare IoMT. The authors urge the standardization of FL protocols to universally assess algorithm performance across diverse healthcare datasets. Additionally, designing FL models resilient to diffused and heterogeneous health data remains a pressing challenge, especially as next-generation networks like 6G commence supporting vast IoMT ecosystems.

In conclusion, while FL offers promising solutions for privacy-preserving healthcare data handling, ongoing research must address practical constraints and continuously refine mechanisms for robust, scalable implementations. Such efforts would contribute significantly to the evolution of smart healthcare services and underpin the sustained integration of IoMT in global health infrastructure.