  As a promising privacy-preserving machine learning method, Federated Learning
(FL) enables global model training across clients without compromising their
confidential local data. However, existing FL methods suffer from the problem
of low inference performance for unevenly distributed data, since most of them
rely on Federated Averaging (FedAvg)-based aggregation. By averaging model
parameters in a coarse manner, FedAvg eclipses the individual characteristics
of local models, which strongly limits the inference capability of FL. Worse
still, in each round of FL training, FedAvg dispatches the same initial local
models to clients, which can easily result in stuck-at-local-search for optimal
global models. To address the above issues, this paper proposes a novel and
effective FL paradigm named FedMR (Federating Model Recombination). Unlike
conventional FedAvg-based methods, the cloud server of FedMR shuffles each
layer of collected local models and recombines them to achieve new models for
local training on clients. Due to the fine-grained model recombination and
local training in each FL round, FedMR can quickly figure out one globally
optimal model for all the clients. Comprehensive experimental results
demonstrate that, compared with state-of-the-art FL methods, FedMR can
significantly improve the inference accuracy without causing extra
communication overhead.
