Combining Data Privacy & Machine Learning
The development of computing capabilities during the last decades has enabled organizations to gather and process large amount of data using advanced machine learning techniques. At the same time, there has been an increased focus on data privacy creating new challenges for businesses which aims to deploy machine learning models.
The importance of data
As the number of connected devices is exploding, the amount of data is expected to grow exponentially in the coming years. Companies are fighting to collect and structure this data to be able to understand the world and predict the future. In many ways, data is driving future innovation with use cases such as fraud detection, autonomous driving, and cancer diagnostics.
The increased focus on data privacy and security
With the introduction of regulations such as GDPR and the increased media attention on data privacy such as with LinkedIn’s recent data breach in 2021, users have become more aware of the amount of data they are sharing daily.
“Organizations must therefore review their complete data management process to ensure they meet regulatory and consumer privacy needs”
One possible solution for organizations to protect their users’ privacy while still leveraging data is through federated learning.
Federated learning as a possible solution
In contrast to traditional centralized machine learning where data is stored and trained on a centralized server, federated learning decentralizes the training by keeping the data stored at the local devices. Instead of sharing data between devices, local models are trained collaboratively by exchanging model parameters.
“Federated learning could become the foundation of next-generation machine learning that caters to technological and societal needs for responsible AI development and application.”
This learning technique makes federated learning a viable option in areas such as telecommunications, healthcare, and loT where sensitive data are spread amongst several local devices. Federated learning applications ranges from adapting to pedestrian behavior and road conditions in autonomous vehicles to developing predictive models to identify diseases in the healthcare industry.
Future of federated learning
Federated learning, introduced by Google in 2017, is still a relatively new research field and yet to be fully deployed in industry. However, with rapid advancements by the research community and an increasing demand from the end users, there is an increasing business potential for organizations to capitalize on.