  As an emerging federated learning paradigm, federated distillation enables
communication-efficient model training by transmitting only small-scale
knowledge during the learning process. To further improve the communication
efficiency of federated distillation, we propose a novel technique, ALU, which
accumulates multiple rounds of local updates before transferring the knowledge
to the central server. ALU drastically decreases the frequency of communication
in federated distillation, thereby significantly reducing the communication
overhead during the training process. Empirical experiments demonstrate the
substantial effect of ALU in improving the communication efficiency of
federated distillation.
