TechDispatch : federated learning

Federated Learning (FL) presents a promising approach to machine learning (ML) by allowing multiple sources of data (devices or entities) to collaboratively train a shared model while keeping data decentralised. This approach mitigates privacy risks as raw data remains locally on the sources, which is particularly beneficial in scenarios where data sensitivity or regulatory requirements make data centralisation impractical. Applications of FL are diverse, spanning personalised recommendations, healthcare data analysis, data spaces, and autonomous transport systems, where ensuring privacy and data protection is paramount. From a personal data protection perspective, FL offers significant benefits by minimising personal data sharing. This decentralised approach aligns with the core principles of data protection, such as data minimisation and purpose limitation, by ensuring that personal data remains under the control of the controller and is not exposed to external parties. Furthermore, FL improves accountability and auditability, as data controllers have clearer oversight of how personal data is processed. Additionally, by keeping raw data on local devices/servers and only sharing models or model updates (gradients or weights), FL can enhance the confidentiality of personal data limiting the need for its centralisation and reducing the impact of large-scale data breaches.