  Communication constraints are one of the major challenges preventing the
wide-spread adoption of Federated Learning systems. Recently, Federated
Distillation (FD), a new algorithmic paradigm for Federated Learning with
fundamentally different communication properties, emerged. FD methods leverage
ensemble distillation techniques and exchange model outputs, presented as soft
labels on an unlabeled public data set, between the central server and the
participating clients. While for conventional Federated Learning algorithms,
like Federated Averaging (FA), communication scales with the size of the
jointly trained model, in FD communication scales with the distillation data
set size, resulting in advantageous communication properties, especially when
large models are trained. In this work, we investigate FD from the perspective
of communication efficiency by analyzing the effects of active
distillation-data curation, soft-label quantization and delta-coding
techniques. Based on the insights gathered from this analysis, we present
Compressed Federated Distillation (CFD), an efficient Federated Distillation
method. Extensive experiments on Federated image classification and language
modeling problems demonstrate that our method can reduce the amount of
communication necessary to achieve fixed performance targets by more than two
orders of magnitude, when compared to FD and by more than four orders of
magnitude when compared with FA.
