Recent Trends of Federated Learning
In recent years, federated learning has accelerated thanks to growing demands for data privacy, edge-AI, and distributed training frameworks. The market is expected to grow rapidly as organisations adopt federated architectures across mobile, IoT and enterprise environments. Key trends include: the surge of edge devices participating in federated rounds, the adoption of frameworks like TensorFlow Federated, PySyft and FATE for privacy-first model training. Industries such as manufacturing are using federated learning for predictive maintenance and supply-chain optimisation, while healthcare and finance leverage it for collaborative modelling across institutions without data sharing.As a certification candidate, keeping abreast of these emerging frameworks and sector-specific deployments will ensure you’re well positioned in an evolving technical landscape.
