Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Wireless Federated Learning over MIMO Networks: Joint Device Scheduling
  and Beamforming Design   Federated learning (FL) is recognized as a key enabling technology to support
distributed artificial intelligence (AI) services in future 6G. By supporting
decentralized data training and collaborative model training among devices, FL
inherently tames privacy leakage and reduces transmission costs. Whereas, the
performance of the wireless FL is typically restricted by the communication
latency. Multiple-input multiple-output (MIMO) technique is one promising
solution to build up a communication-efficient edge FL system with limited
radio resources. In this paper, we propose a novel joint device scheduling and
receive beamforming design approach to reduce the FL convergence gap over
shared wireless MIMO networks. Specifically, we theoretically establish the
convergence analysis of the FL process, and then apply the proposed device
scheduling policy to maximize the number of weighted devices under the FL
system latency and sum power constraints. Numerical results verify the
theoretical analysis of the FL convergence and exhibit the appealing learning
performance of the proposed approach.

24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn
2
The categories of this paper are: math
15
The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc
25
The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
10
The categories of this paper are: q-bio
27
The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th
29
The categories of this paper are: funct-an