Mixed-Precision Quantization for Federated Learning on
  Resource-Constrained Heterogeneous Devices
Huancheng Chen Haris Vikalo
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.