Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Computational Intelligence and Deep Learning for Next-Generation
  Edge-Enabled Industrial IoT   In this paper, we investigate how to deploy computational intelligence and
deep learning (DL) in edge-enabled industrial IoT networks. In this system, the
IoT devices can collaboratively train a shared model without compromising data
privacy. However, due to limited resources in the industrial IoT networks,
including computational power, bandwidth, and channel state, it is challenging
for many devices to accomplish local training and upload weights to the edge
server in time. To address this issue, we propose a novel multi-exit-based
federated edge learning (ME-FEEL) framework, where the deep model can be
divided into several sub-models with different depths and output prediction
from the exit in the corresponding sub-model. In this way, the devices with
insufficient computational power can choose the earlier exits and avoid
training the complete model, which can help reduce computational latency and
enable devices to participate into aggregation as much as possible within a
latency threshold. Moreover, we propose a greedy approach-based exit selection
and bandwidth allocation algorithm to maximize the total number of exits in
each communication round. Simulation experiments are conducted on the classical
Fashion-MNIST dataset under a non-independent and identically distributed
(non-IID) setting, and it shows that the proposed strategy outperforms the
conventional FL. In particular, the proposed ME-FEEL can achieve an accuracy
gain up to 32.7% in the industrial IoT networks with the severely limited
resources.

2
The categories of this paper are: math
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
20
The categories of this paper are: acc-phys
23
The categories of this paper are: solv-int
25
The categories of this paper are: patt-sol
13
The categories of this paper are: quant-ph
9
The categories of this paper are: nlin
24
The categories of this paper are: dg-ga
17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
4
The categories of this paper are: cond-mat
16
The categories of this paper are: stat
21
The categories of this paper are: adap-org