Go Federated with OpenFL

OpenFL is an open-source framework for Federated Learning (FL) developed at Intel. FL is a technique for training statistical models on sharded datasets, distributed across several nodes. Moreover, data may be not identically distributed between different shards and cannot be moved between nodes, due to privacy / legal concerns (laws such as HIPAA or GDPR), size of the dataset, or other reasons. OpenFL is designed to solve so-called cross-silo federated learning problems when data is split between organizations or remote data centers. OpenFL aims to provide an effective and secure infrastructure for data scientists. With the v1.2 update OpenFL team endeavors to raise the framework's learnability and decouple the procedure of setting up the Federation and using it to run FL experiments.