Accessible Ecosystem for Clinical Research (Federated Learning for Everyone): Development and Usability Study

Abstract

Background: The integrity and reliability of clinical research outcomes rely heavily on access to vast amounts of data. However, the fragmented distribution of these data across multiple institutions, along with ethical and regulatory barriers, presents significant challenges to accessing relevant data. While federated learning offers a promising solution to leverage insights from fragmented data sets, its adoption faces hurdles due to implementation complexities, scalability issues, and inclusivity challenges.

Objective: This paper introduces Federated Learning for Everyone (FL4E), an accessible framework facilitating multistakeholder collaboration in clinical research. It focuses on simplifying federated learning through an innovative ecosystem-based approach.

Methods: The "degree of federation" is a fundamental concept of FL4E, allowing for flexible integration of federated and centralized learning models. This feature provides a customizable solution by enabling users to choose the level of data decentralization based on specific health care settings or project needs, making federated learning more adaptable and efficient. By using an ecosystem-based collaborative learning strategy, FL4E encourages a comprehensive platform for managing real-world data, enhancing collaboration and knowledge sharing among its stakeholders.

Results: Evaluating FL4E's effectiveness using real-world health care data sets has highlighted its ecosystem-oriented and inclusive design. By applying hybrid models to 2 distinct analytical tasks-classification and survival analysis-within real-world settings, we have effectively measured the "degree of federation" across various contexts. These evaluations show that FL4E's hybrid models not only match the performance of fully federated models but also avoid the substantial overhead usually linked with these models. Achieving this balance greatly enhances collaborative initiatives and broadens the scope of analytical possibilities within the ecosystem.

Conclusions: FL4E represents a significant step forward in collaborative clinical research by merging the benefits of centralized and federated learning. Its modular ecosystem-based design and the "degree of federation" feature make it an inclusive, customizable framework suitable for a wide array of clinical research scenarios, promising to revolutionize the field through improved collaboration and data use. Detailed implementation and analyses are available on the associated GitHub repository.

Keywords: accessible; clinical research; design effectiveness; ecosystem; federated learning; implementation; inclusive; inclusivity; integrity; multistakeholder collaboration; real-world data; reliability.

Figure 1

The degree of federation characterizes the balance between fully centralized and fully federated setups, leading to hybrid solutions where some of the stakeholders centralize their data while others prefer a federated approach. FL4E: Federated Learning for Everyone.

Figure 2

FL4E’s nested approach schema: the schema presents the ecosystem approach of the FL4E, facilitating the incorporation of diverse studies framed by different research questions. It further highlights the support for a wide range of analyses, leading to the execution of statistical experiments. FL4E: Federated Learning for Everyone.

Figure 3

The high-level architecture of FL4E: This figure showcases a comprehensive framework designed to accommodate the nuanced interactions of diverse stakeholders. The architecture includes the expected user stories across 3 primary categories of participants: data providers, data scientists, and downstream users. Each stakeholder group engages with FL4E platform through distinct pathways. The architecture diagram features a table outlining the unique interactions of the stakeholders, mapping their respective roles and activities within the FL4E framework. This detailed mapping clearly explains how each stakeholder contributes to and benefits from the FL4E, highlighting the platform's versatility and user-centric design. At the core of our architecture lie 3 fundamental components: the server, client, and executor machine, each vital to the execution of FL tasks. The diagram we provide elucidates their interconnected roles, showcasing the seamless flow of data, scripts, and analytical results across the system. The server acts as the orchestrator for FL tasks, hosting the primary web application within a Docker container as an ASP.NET application. It securely manages the platform's data, housed on an SQL server in a dedicated hosting environment. This component is crucial for coordinating tasks and uses a Python environment to manage secure data sharing and preprocessing of “data center” module of the framework. On the client side, implemented as a Docker-based image, it runs on the data contributor's machine. This component is essential for integrating RWD into the FL process. Developed using Python and ASP.NET for web applications, the client-side component establishes a connection to the executer machine. On the other hand, the executor machine plays a crucial role in conducting the analysis. It is designed to receive client updates. This adaptable component configuration allows data scientists to tailor it according to their specific analytical needs and preferences. FL: federated learning; FL4E: Federated Learning for Everyone; RWD: real-world data.