While federated learning (FL) systems often utilize quantization to battle
communication and computational bottlenecks, they have heretofore been limited
to deploying fixed-precision quantization schemes. Meanwhile, the concept of
mixed-precision quantization (MPQ), where different layers of a deep learning
model are assigned varying bit-width, remains unexplored in the FL settings. We
present a novel FL algorithm, FedMPQ, which introduces mixed-precision
quantization to resource-heterogeneous FL systems. Specifically, local models,
quantized so as to satisfy bit-width constraint, are trained by optimizing an
objective function that includes a regularization term which promotes reduction
of precision in some of the layers without significant performance degradation.
The server collects local model updates, de-quantizes them into full-precision
models, and then aggregates them into a global model. To initialize the next
round of local training, the server relies on the information learned in the
previous training round to customize bit-width assignments of the models
delivered to different clients. In extensive benchmarking experiments on
several model architectures and different datasets in both iid and non-iid
settings, FedMPQ outperformed the baseline FL schemes that utilize
fixed-precision quantization while incurring only a minor computational
overhead on the participating devices.

引入混合精度量化方法到异构资源联邦学习系统中以解决通信和计算瓶颈问题，并在多个模型架构和数据集上进行了广泛的基准性实验验证其优于固定精度量化的性能。