Federated machine learning promises to overcome emerging privacy challenges. Hence, algorithmic aspects of the topic have gained popularity in the scientific literature. However, fundamental aspects such as scalability, robustness, security, and performance in a geographically-distributed setting remain relatively unexplored.
At Scaleout Systems, they are developing an open-core platform for federated machine learning operations that aims at bridging the gap between the scientific literature and real-world deployments. The aim of this talk is to share challenges and experiences in their development journey.