Federated learning is a type of decentralised machine learning where local computing power (on individual machines) are used to train models on individually owned datasets, merged into a global model.
This can create a more accurate model, since the collaborators pool together data to form a bigger dataset.
A key problem is that collaborators may be bad actors, and private data could be contained within the local data that’s trained into a model that’s uploaded.