Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge (2101.07511v1)

Abstract: Despite significant improvements over the last few years, cloud-based healthcare applications continue to suffer from poor adoption due to their limitations in meeting stringent security, privacy, and quality of service requirements (such as low latency). The edge computing trend, along with techniques for distributed machine learning such as federated learning, have gained popularity as a viable solution in such settings. In this paper, we leverage the capabilities of edge computing in medicine by analyzing and evaluating the potential of intelligent processing of clinical visual data at the edge allowing the remote healthcare centers, lacking advanced diagnostic facilities, to benefit from the multi-modal data securely. To this aim, we utilize the emerging concept of clustered federated learning (CFL) for an automatic diagnosis of COVID-19. Such an automated system can help reduce the burden on healthcare systems across the world that has been under a lot of stress since the COVID-19 pandemic emerged in late 2019. We evaluate the performance of the proposed framework under different experimental setups on two benchmark datasets. Promising results are obtained on both datasets resulting in comparable results against the central baseline where the specialized models (i.e., each on a specific type of COVID-19 imagery) are trained with central data, and improvements of 16\% and 11\% in overall F1-Scores have been achieved over the multi-modal model trained in the conventional Federated Learning setup on X-ray and Ultrasound datasets, respectively. We also discuss in detail the associated challenges, technologies, tools, and techniques available for deploying ML at the edge in such privacy and delay-sensitive applications.

Collaborative Federated Learning for Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge

The paper "Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge" explores the convergence of edge computing and federated learning (FL) to improve the efficiency and security of healthcare systems, particularly in diagnosing COVID-19 from clinical images. The paper highlights how traditional cloud-based healthcare applications have struggled to meet necessary privacy, security, and latency requirements. Edge computing, combined with FL, presents a promising solution to these challenges by enabling localized data processing and minimizing the need for data transfer to centralized servers.

Overview

The authors propose leveraging clustered federated learning (CFL) to enable remote healthcare centers to collaboratively diagnose COVID-19 using multi-modal imaging data such as X-ray and ultrasound, without compromising data privacy or security. This approach aims to address the resource limitations and privacy concerns that have hampered wider adoption of cloud-based healthcare solutions.

Key Findings

The paper outlines the implementation of CFL in a two-cluster setup, each containing clients with different imaging modalities: X-ray and ultrasound. A shared machine learning model is trained collaboratively across these clusters, allowing the development of a multi-modal diagnostic tool. The proposed CFL framework demonstrates superior performance in comparison to conventional FL, achieving improvements in F1-Scores of 16% and 11% for X-ray and ultrasound datasets, respectively. This emphasizes CFL's ability to handle divergence in data distributions due to varying sources and modalities.

Implications and Future Directions

Practical Implications: The implementation of CFL in healthcare settings offers significant advantages in terms of data privacy and reduced latency, which are critical in telemedicine applications. Edge computing facilitates real-time processing, which is advantageous for delay-sensitive applications in healthcare.

Theoretical Implications: The paper contributes to the understanding of federated learning systems, especially in managing statistical heterogeneity and optimizing collaborative learning protocols. It opens avenues for refining CFL techniques to better suit multi-modal tasks and varying data distributions.

Future Development in AI: The insights gathered on CFL's performance suggest a potential expansion of such frameworks to other domains where data privacy is paramount, and centralized data storage is impractical. Future research could explore adaptive and personalized models tailored to individual healthcare entities' specific data characteristics, further optimizing collaborative learning processes.

Challenges and Open Research Issues

The paper identifies several challenges and areas for future research. Resource scarcity and heterogeneity among edge devices pose significant obstacles to ML deployment. Network communication stability might affect performance, highlighting the necessity for robust asynchronous FL methods. Security concerns remain prevalent due to potential adversarial attacks, warranting attention on adversarially robust ML models. Explorations into decentralized computing with blockchain and optimization of hardware and algorithmic resources could further enhance CFL's efficacy and applicability in healthcare settings.

In conclusion, this paper provides an insightful proposition for merging edge computing with federated learning in healthcare, presenting a feasible pathway to multi-modal COVID-19 diagnosis while safeguarding data privacy and security. The promising results underscore the importance of continued innovation in collaborative learning technologies, paving the way for transformative changes in healthcare delivery and beyond.