Is the Future of AI Really Federated? Federated Learning as an Emerging Market

The paradigm of centralized training of machine learning (ML) models has come under increasing criticism in recent times. Federated learning (FL) offers a technological extension or even alternative that allows AI models to be trained in a decentralized manner while maintaining data protection requirements and the confidentiality of company data. This opens up a new data space for training specialized, domain-specific ML models in particular. And there are already voices saying that the future of AI is federated. However, it is unclear to what extent FL will actually develop into a real market. In this overview article we analyze the uncertainties that currently characterize FL in a systematic way distinguishing four areas of uncertainty: technology, users & use cases, privacy regulation & IT-security, and suppliers & the FL ecosystem. We conclude that there are clear indications that FL is on its way to become an emerging market. Especially in the application fields of healthcare, banking and manufacturing, FL can solve problems that other privacy-enhancing technologies (PETs) are currently unable to solve. On the other hand, further research is needed to ultimately turn FL into an turnkey application that is easy to deploy for industrial end customers and service providers. Big tech companies could give FL an additional boost in the future by actively embracing the trend towards domain-specific fine-tuning of AI models.