The Impact of Federated Learning on AI-Enhanced Healthcare Delivery

Introduction

In the past decades, the integration of Artificial Intelligence (AI) in healthcare has emerged as a promising way to transform the journey of medical practices, treatment, analysis and diagnosis of patients. With the planned innovative paradigm, the focus of healthcare is developing; Federated Learning stands out on the mark to plan the groundbreaking approach and enable the high pace growth to the robust AI models while the increased dedication towards the data authenticity, growth, and confidentiality. The study research will aim to explore the synergies between Federated Learning and AI integration in the area of healthcare, elucidating on the gains, accessing the issues, and studying the potential gains it can lead to embracing the patient health improvements and having more impactful treatment.

The traditional model of AI as integrated into the healthcare segment relies on the centralised focus on information, which poses a significant impact on the connections with the increased data confidentiality, data growth, security, and increased potential impact overall. The study is about Federated Learning is on addressing the issues of decentralization of the processes. This study will allow the multiple areas to collaboratively work to train the models while keeping the data and work managed in healthcare.

Organization of the Article

Figure 1.

Organization of the article

Source: Self-created, influenced by Rani et al. (2023)

Literature Review

According to Rehman et al. (2022), In the modern world, it is very challenging to transport patients both from their homes and even from the hospitals to get in the routine checkups. Queuing, travel time, and the risk of viral transmission for patients, as they move through this dirty environment are just a few of the difficulties. Because of this, the healthcare sector is putting more emphasis on at-home healthcare services, which let people get checked out in the convenience of their own homes. In order to connect patients in rural places with doctors, a smart health monitoring system is built in areas in cities. This technology serves as a facilitator for communication between patients and clinicians. It monitors physiological parameters like heart rate and electrocardiogram (ECG), heart rate, body temperature, and whether or not a person has fallen. This data is gathered and sent by the system.

Yoo et al. (2022), in their research, mentioned that a machine learning approach called federated Learning, which is based on a distributed data environment, has become more popular as a result of stronger data protection legislation everywhere in the globe. The General Data Protection Regulation of the EU and the Privacy Rights Act of California (CA) were in place when the idea of federated Learning was initially proposed. 2017 saw the launch of Google's federated Learning, allowing AI to learn without utilising regional data. Particularly in the sphere of medicine, it has been actively explored. From the perspective of data privacy, training approaches without client data gathering are a desirable benefit. However, because of their peculiarities, such as varied distributions of data, client structures, and even susceptible training environments, Federated learning techniques continue to pose several unresolved issues and concerns.

According to Rahman et al. (2023), Federated learning offers an analytical remedy to the issues that machine learning techniques now have with data privacy violations. In comparison to centralised artificial intelligence, it has a distinct structure and traits. When using traditional machine learning techniques, the server needs to be supplied with a significant amount of training data that has been acquired from local data owners. With the aid of federated Learning, in a distributed learning framework, deep neural network models are generated and developed without the need for local data collection and transmission to a server.