  Federated Learning (FL) is a machine learning setting where many devices
collaboratively train a machine learning model while keeping the training data
decentralized. In most of the current training schemes the central model is
refined by averaging the parameters of the server model and the updated
parameters from the client side. However, directly averaging model parameters
is only possible if all models have the same structure and size, which could be
a restrictive constraint in many scenarios.
  In this work we investigate more powerful and more flexible aggregation
schemes for FL. Specifically, we propose ensemble distillation for model
fusion, i.e. training the central classifier through unlabeled data on the
outputs of the models from the clients. This knowledge distillation technique
mitigates privacy risk and cost to the same extent as the baseline FL
algorithms, but allows flexible aggregation over heterogeneous client models
that can differ e.g. in size, numerical precision or structure. We show in
extensive empirical experiments on various CV/NLP datasets (CIFAR-10/100,
ImageNet, AG News, SST2) and settings (heterogeneous models/data) that the
server model can be trained much faster, requiring fewer communication rounds
than any existing FL technique so far.
