Machine learning has made huge strides in recent years, allowing organizations to train models that can recognize patterns in vast amounts of data that, until now, have been difficult to navigate. But, as the amount of data grows, so does the challenge of processing it; federated learning is a decentralized approach to machine learning that aims to help solve this problem.
For federated learning to be adopted, data custodians must make data available without sharing that data or moving it to a central location. In doing so, they can allow data scientists and machine learning engineers to run advanced analytics and train models in a federated (ie. distributed) manner.
Thanks to technological advances, large corporations and startups alike are working to make federated learning ubiquitous so that companies – and consumers too – can benefit.
According to a new research report from Polaris Market Research, the global federated learning market was valued at USD 110.82 million in 2021 and is expected to reach USD 266.77 million by 2030, growing at a CAGR of 10.7% throughout the forecast period.
A Powerful Present
Unlike machine learning techniques that utilize centralized data, stored in a silo, federated learning operates differently by enabling collaborative learning among distributed data sources without pooling data centrally.
Although still in the early stages, federated learning has already experienced remarkable growth and innovation, signalling its arrival as a viable solution with a highly attractive value proposition. This includes its ability to help organizations comply with ever-increasing data privacy and AI regulation, which typically dictates that sensitive data cannot be moved from the territory it was originally created in. It achieves this by allowing computational access to data while custodians remain in full control of their data, allowing them to comply with data privacy regulation requirements and protect their intellectual property (IP) without requiring them to share or centralize it. By removing the need to move data outside of their environment, security is greatly improved while the risk of data breaches, IP leaks, and unauthorized data access is reduced.
Not only is this beneficial from a privacy standpoint, it offers many additional benefits; the cost of copying and centralizing data is completely removed, while accuracy is drastically improved thanks to the availability of sensitive, previously siloed data that bolsters ML models or analytics.
Combined, this has led to rapid innovation and growth in a plethora of industries. Medical imagery, for example, is a data-intensive source that benefits from federated-learning-powered AI. By harnessing the power of AI, medical professionals can achieve a new level of analytical depth and improve the efficiency and precision of image processing.
Radiologists, in particular, face an exponential increase in their workload, and often need to read medical images in a matter of a few seconds to meet the demands on their time. This intensity can negatively affect their mental state and concentration, leading to reduced diagnostic accuracy, additional stress for patients, and burnout for healthcare specialists.
Leveraging federated learning to address these challenges, AI-driven applications can be developed that are capable of improving diagnostic accuracy while providing workflow support previously thought impossible. Moreover, AI can detect abnormalities in medical images that the human eye might miss or misinterpret, unlocking new possibilities for early detection and treatment.
Medtech, in general, is also benefiting from federated learning. For example, by training and evaluating language models on sensitive medical information and health data, telemedicine and patient apps are developed without exposing personal data and are more accurate, predictable and trustworthy as a result of access to more complete, representative data. In healthcare, AI has the potential to revolutionize the way medical professionals treat patients. For instance, it can be used to create models that accelerate the drug discovery process, the development of diagnostics, or clinical decision support applications, ultimately leading to better diagnosis and treatments.
Manufacturers can reap significant benefits by leveraging federated learning and the data collaboration it enables, unearthing collective insights and identifying new revenue streams for their data assets. This collaborative environment can be used to train models to perform predictive maintenance and optimize their manufacturing processes to reduce downtime and related costs. Additionally, smart manufacturing can enhance productivity, maximize efficiencies, and reduce waste in the manufacturing process by utilizing data insights.
However, the most critical opportunity lies in leveraging data collaboration to accelerate the industry's journey towards achieving net-zero emissions across its supply chains. By working with suppliers, for example, the flow of goods and materials through the supply chain can be optimized, reducing transportation costs, minimizing waste, and improving efficiency
A Bright Future
Federated learning has the potential to revolutionize the way we think about machine learning by allowing organizations to train models with greater accuracy and privacy. By decentralizing the process, federated learning also gives companies greater control over their data.
It's evident that the time has come for organizations to embrace federated learning as a tool that’s able to unlock rapid innovation and significant growth.
What is federated ML and analytics and why does it matter?