Federated Learning (FL) will be a common approach used for many distributed AI applications to achieve privacy, where different devices collaborate on the development of ML models without sharing their private data. FL approach will enable distributed systems to collaborate to devise more intelligent solutions, yet it brings several challenges, which needs to be investigated and resolved, such as the impact of the heterogeneity in the devices' computational capabilities or in their communication link quality.

We offer our expertise in EU projects developing FL extensions to accelerator-based Deep Learning frameworks, 5G IoT systems using FL, etc.

Looking forward to devise the AI solutions of the future together...