Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Federated Learning with Matched Averaging   Federated learning allows edge devices to collaboratively learn a shared
model while keeping the training data on device, decoupling the ability to do
model training from the need to store the data in the cloud. We propose
Federated matched averaging (FedMA) algorithm designed for federated learning
of modern neural network architectures e.g. convolutional neural networks
(CNNs) and LSTMs. FedMA constructs the shared global model in a layer-wise
manner by matching and averaging hidden elements (i.e. channels for convolution
layers; hidden states for LSTM; neurons for fully connected layers) with
similar feature extraction signatures. Our experiments indicate that FedMA not
only outperforms popular state-of-the-art federated learning algorithms on deep
CNN and LSTM architectures trained on real world datasets, but also reduces the
overall communication burden.

31
The categories of this paper are: ao-sci
18
The categories of this paper are: econ
12
The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg
4
The categories of this paper are: cond-mat
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
25
The categories of this paper are: patt-sol
14
The categories of this paper are: math-ph
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
32
The categories of this paper are: atom-ph
9
The categories of this paper are: nlin
20
The categories of this paper are: acc-phys