  Personalized decision-making can be implemented in a Federated learning (FL)
framework that can collaboratively train a decision model by extracting
knowledge across intelligent clients, e.g. smartphones or enterprises. FL can
mitigate the data privacy risk of collaborative training since it merely
collects local gradients from users without access to their data. However, FL
is fragile in the presence of statistical heterogeneity that is commonly
encountered in personalized decision-making, e.g., non-IID data over different
clients. Existing FL approaches usually update a single global model to capture
the shared knowledge of all users by aggregating their gradients, regardless of
the discrepancy between their data distributions. By comparison, a mixture of
multiple global models could capture the heterogeneity across various clients
if assigning the client to different global models (i.e., centers) in FL. To
this end, we propose a novel multi-center aggregation mechanism to cluster
clients using their models' parameters. It learns multiple global models from
data as the cluster centers, and simultaneously derives the optimal matching
between users and centers. We then formulate it as an optimization problem that
can be efficiently solved by a stochastic expectation maximization (EM)
algorithm. Experiments on multiple benchmark datasets of FL show that our
method outperforms several popular baseline methods. The experimental source
codes are publicly available on the Github repository
https://github.com/mingxuts/multi-center-fed-learning .
