  Federated learning has allowed the training of statistical models over remote
devices without the transfer of raw client data. In practice, training in
heterogeneous and large networks introduce novel challenges in various aspects
like network load, quality of client data, security and privacy. Recent works
in FL have worked on improving communication efficiency and addressing uneven
client data distribution independently, but none have provided a unified
solution for both challenges. We introduce a new family of Federated Learning
algorithms called CatFedAvg which not only improves the communication
efficiency but improves the quality of learning using a category coverage
maximization strategy.
  We use the FedAvg framework and introduce a simple and efficient step every
epoch to collect meta-data about the client's training data structure which the
central server uses to request a subset of weight updates. We explore two
distinct variations which allow us to further explore the tradeoffs between
communication efficiency and model accuracy. Our experiments based on a vision
classification task have shown that an increase of 10% absolute points in
accuracy using the MNIST dataset with 70% absolute points lower network
transfer over FedAvg. We also run similar experiments with Fashion MNIST,
KMNIST-10, KMNIST-49 and EMNIST-47. Further, under extreme data imbalance
experiments for both globally and individual clients, we see the model
performing better than FedAvg. The ablation study further explores its
behaviour under varying data and client parameter conditions showcasing the
robustness of the proposed approach.
