Federated Learning for Smart Healthcare: A Survey

Recent advances in communication technologies and the Internet-of-Medical-Things (IOMT) have transformed smart healthcare enabled by artificial intelligence (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.

The practicality and prospects for disinfection control by photocatalysis during and post-pandemic: A critical review.

The prevalence of global health implications from the COVID-19 pandemic necessitates the innovation and large-scale application of disinfection technologies for contaminated surfaces, air, and wastewater as the significant transmission media of disease. To date, primarily recommended disinfection practices are energy exhausting, chemical driven, and cause severe impact on the environment. The research on advanced oxidation processes has been recognized as promising strategies for disinfection purposes. In particular, semiconductor-based photocatalysis is an effective renewable solar-driven technology that relies on the reactive oxidative species, mainly hydroxyl (•OH) and superoxide (•O2-) radicals, for rupturing the capsid shell of the virus and loss of pathogenicity. However, the limited understanding of critical aspects such as viral photo-inactivation mechanism, rapid virus mutagenicity, and virus viability for a prolonged time restricts the large-scale application of photocatalytic disinfection technology. In this work, fundamentals of photocatalysis disinfection phenomena are addressed with a reviewed remark on the reported literature of semiconductor photocatalysts efficacies against SARS-CoV-2. Furthermore, to validate the photocatalysis process on an industrial scale, we provide updated data on available commercial modalities for an effective virus photo-inactivation process. An elaborative discussion on the long-term challenges and sustainable solutions is suggested to fill in the existing knowledge gaps. We anticipate this review will ignite interest among researchers to pave the way to the photocatalysis process for disinfecting virus-contaminated environments and surfaces for current and future pandemics.

Federated Learning for COVID-19 Detection with Generative Adversarial Networks in Edge Cloud Computing

COVID-19 has spread rapidly across the globe and become a deadly pandemic. Recently, many artificial intelligence-based approaches have been used for COVID-19 detection, but they often require public data sharing with cloud datacentres and thus remain privacy concerns. This paper proposes a new federated learning scheme, called FedGAN, to generate realistic COVID-19 images for facilitating privacy-enhanced COVID-19 detection with generative adversarial networks (GANs) in edge cloud computing. Particularly, we first propose a GAN where a discriminator and a generator based on convolutional neural networks (CNNs) at each edge-based medical institution alternatively are trained to mimic the real COVID-19 data distribution. Then, we propose a new federated learning solution which allows local GANs to collaborate and exchange learned parameters with a cloud server, aiming to enrich the global GAN model for generating realistic COVID-19 images without the need for sharing actual data. To enhance the privacy in federated COVID-19 data analytics, we integrate a differential privacy solution at each hospital institution. Moreover, we propose a new blockchain-based FedGAN framework for secure COVID-19 data analytics, by decentralizing the FL process with a new mining solution for low running latency. Simulations results demonstrate the superiority of our approach for COVID-19 detection over the state-of-the-art schemes. IEEE