Systems and methods for tiering data in distributed data networks. A global model is developed based on federated learning where edge servers are able to train a model. The learning from the edge servers are collectively applied to the global model. This process can be repeated until the global model is ready for deployment. The global model allows data to be tiered. This may include pushing data from a datacenter to edge servers or cleaning edge servers of data that does not satisfy the global model. The model can be retrained and can be used to proactively push new content out to the edge servers.