  Federated Learning (FL) is currently the most widely adopted framework for
collaborative training of (deep) machine learning models under privacy
constraints. Albeit it's popularity, it has been observed that Federated
Learning yields suboptimal results if the local clients' data distributions
diverge. To address this issue, we present Clustered Federated Learning (CFL),
a novel Federated Multi-Task Learning (FMTL) framework, which exploits
geometric properties of the FL loss surface, to group the client population
into clusters with jointly trainable data distributions. In contrast to
existing FMTL approaches, CFL does not require any modifications to the FL
communication protocol to be made, is applicable to general non-convex
objectives (in particular deep neural networks) and comes with strong
mathematical guarantees on the clustering quality. CFL is flexible enough to
handle client populations that vary over time and can be implemented in a
privacy preserving way. As clustering is only performed after Federated
Learning has converged to a stationary point, CFL can be viewed as a
post-processing method that will always achieve greater or equal performance
than conventional FL by allowing clients to arrive at more specialized models.
We verify our theoretical analysis in experiments with deep convolutional and
recurrent neural networks on commonly used Federated Learning datasets.
