We study federated learning (FL), which enables mobile devices to utilize
their local datasets to collaboratively train a global model with the help of a
central server, while keeping data localized. At each iteration, the server
broadcasts the current global model to the devices for local training, and
aggregates the local model updates from the devices to update the global model.
Previous work on the communication efficiency of FL has mainly focused on the
aggregation of model updates from the devices, assuming perfect broadcasting of
the global model. In this paper, we instead consider broadcasting a compressed
version of the global model. This is to further reduce the communication cost
of FL, which can be particularly limited when the global model is to be
transmitted over a wireless medium. We introduce a lossy FL (LFL) algorithm, in
which both the global model and the local model updates are quantized before
being transmitted. We analyze the convergence behavior of the proposed LFL
algorithm assuming the availability of accurate local model updates at the
server. Numerical experiments show that the proposed LFL scheme, which
quantizes the global model update (with respect to the global model estimate at
the devices) rather than the global model itself, significantly outperforms
other existing schemes studying quantization of the global model at the
PS-to-device direction. Also, the performance loss of the proposed scheme is
marginal compared to the fully lossless approach, where the PS and the devices
transmit their messages entirely without any quantization.

本文提出一种基于压缩全局模型的损失型联邦学习算法（LFL），该算法相对于完全无损方法，使用较少的通信资源来实现相同的收敛性。