Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: FedCluster: Boosting the Convergence of Federated Learning via
  Cluster-Cycling   We develop FedCluster--a novel federated learning framework with improved
optimization efficiency, and investigate its theoretical convergence
properties. The FedCluster groups the devices into multiple clusters that
perform federated learning cyclically in each learning round. Therefore, each
learning round of FedCluster consists of multiple cycles of meta-update that
boost the overall convergence. In nonconvex optimization, we show that
FedCluster with the devices implementing the local {stochastic gradient descent
(SGD)} algorithm achieves a faster convergence rate than the conventional
{federated averaging (FedAvg)} algorithm in the presence of device-level data
heterogeneity. We conduct experiments on deep learning applications and
demonstrate that FedCluster converges significantly faster than the
conventional federated learning under diverse levels of device-level data
heterogeneity for a variety of local optimizers.

26
The categories of this paper are: bayes-an
15
The categories of this paper are: nucl-ex
22
The categories of this paper are: chao-dyn
11
The categories of this paper are: hep-lat
33
The categories of this paper are: plasm-ph
14
The categories of this paper are: math-ph
8
The categories of this paper are: hep-ex
19
The categories of this paper are: eess
34
The categories of this paper are: chem-ph
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
20
The categories of this paper are: acc-phys
12
The categories of this paper are: nucl-th
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph