  Learning image representations on decentralized data can bring many benefits
in cases where data cannot be aggregated across data silos. Softmax cross
entropy loss is highly effective and commonly used for learning image
representations. Using a large number of classes has proven to be particularly
beneficial for the descriptive power of such representations in centralized
learning. However, doing so on decentralized data with Federated Learning is
not straightforward as the demand on FL clients' computation and communication
increases proportionally to the number of classes. In this work we introduce
federated sampled softmax (FedSS), a resource-efficient approach for learning
image representation with Federated Learning. Specifically, the FL clients
sample a set of classes and optimize only the corresponding model parameters
with respect to a sampled softmax objective that approximates the global full
softmax objective. We examine the loss formulation and empirically show that
our method significantly reduces the number of parameters transferred to and
optimized by the client devices, while performing on par with the standard full
softmax method. This work creates a possibility for efficiently learning image
representations on decentralized data with a large number of classes under the
federated setting.
