Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Resource Allocation in Mobility-Aware Federated Learning Networks: A
  Deep Reinforcement Learning Approach   Federated learning allows mobile devices, i.e., workers, to use their local
data to collaboratively train a global model required by the model owner.
Federated learning thus addresses the privacy issues of traditional machine
learning. However, federated learning faces the energy constraints of the
workers and the high network resource cost due to the fact that a number of
global model transmissions may be required to achieve the target accuracy. To
address the energy constraint, a power beacon can be used that recharges energy
to the workers. However, the model owner may need to pay an energy cost to the
power beacon for the energy recharge. To address the high network resource
cost, the model owner can use a WiFi channel, called default channel, for the
global model transmissions. However, communication interruptions may occur due
to the instability of the default channel quality. For this, special channels
such as LTE channels can be used, but this incurs channel cost. As such, the
problem of the model owner is to decide amounts of energy recharged to the
workers and to choose channels used to transmit its global model to the workers
to maximize the number of global model transmissions while minimizing the
energy and channel costs. This is challenging for the model owner under the
uncertainty of the channel, energy and mobility states of the workers. In this
paper, we thus propose to employ the Deep Q-Network (DQN) that enables the
model owner to find the optimal decisions on the energy and the channels
without any a priori network knowledge. Simulation results show that the
proposed DQN always achieves better performance compared to the conventional
algorithms.

28
The categories of this paper are: alg-geom
3
The categories of this paper are: physics
31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph
17
The categories of this paper are: q-fin
6
The categories of this paper are: astro-ph
5
The categories of this paper are: gr-qc
16
The categories of this paper are: stat
10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
11
The categories of this paper are: hep-lat
0
The categories of this paper are: hep-ph
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
29
The categories of this paper are: funct-an