Implicit model specialization through dag-based decentralized federated learning
Jossekin Beilharz, Bjarne Pfitzner, Robert Schmid, Paul Geppert, Bert Arnrich, Andreas Polze · 2021
Federated learning allows a group of distributed clients to train a common machine learning model on private data. The exchange of model updates is managed either by a central entity or in a decentralized way, e.g. by a blockchain. However, the strong generalization across all clients makes these approaches unsuited for non-independent and identically distributed (non-IID) data.