Rethinking federated learning as a digital platform for dynamic and value-driven participation

Abstract

Federated learning (FL) has emerged as a powerful framework for privacy-preserving machine learning, especially relevant in fields like healthcare, finance, and mobile devices. Despite its success, traditional FL systems have a significant limitation: they rely on a static set of clients, forming a federation at the beginning of the training process, which remains fixed throughout the training cycle, thus limiting their scalability and adaptability in dynamic, data-rich settings. To address this, we introduce the concept of federated learning platforms (FLPs), which extend FL into a dynamic platform where client participation is continuously adapted based on their expected value and strategic incentives. In this paper, we envision FLPs as a natural extension of conventional FL that resemble dynamic, value-driven digital platforms where participants can join or leave the federation at any time. Given this dynamicity of client participation, FLPs are designed to gracefully handle changes in the client pool to uphold their value proposition. In this article, we propose a framework for implementing FLPs, outlining key components such as those for dynamic FLP governance, including client on- and offboarding as well as process monitoring. Furthermore, we demonstrate the practical viability of FLPs through a proof of concept for an exemplary use-case and discuss key challenges related to federation stability, data interoperability, as well as privacy, alongside potential solutions. Finally, we present a roadmap and future research directions, guiding the development of robust and scalable FLPs to drive innovation in FL and data interoperability.