  We present a federated learning approach for learning a client adaptable,
robust model when data is non-identically and non-independently distributed
(non-IID) across clients. By simulating heterogeneous clients, we show that
adding learned client-specific conditioning improves model performance, and the
approach is shown to work on balanced and imbalanced data set from both audio
and image domains. The client adaptation is implemented by a conditional gated
activation unit and is particularly beneficial when there are large differences
between the data distribution for each client, a common scenario in federated
learning.
