Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Server Free Wireless Federated Learning: Architecture, Algorithm, and
  Analysis   We demonstrate that merely analog transmissions and match filtering can
realize the function of an edge server in federated learning (FL). Therefore, a
network with massively distributed user equipments (UEs) can achieve
large-scale FL without an edge server. We also develop a training algorithm
that allows UEs to continuously perform local computing without being
interrupted by the global parameter uploading, which exploits the full
potential of UEs' processing power. We derive convergence rates for the
proposed schemes to quantify their training efficiency. The analyses reveal
that when the interference obeys a Gaussian distribution, the proposed
algorithm retrieves the convergence rate of a server-based FL. But if the
interference distribution is heavy-tailed, then the heavier the tail, the
slower the algorithm converges. Nonetheless, the system run time can be largely
reduced by enabling computation in parallel with communication, whereas the
gain is particularly pronounced when communication latency is high. These
findings are corroborated via excessive simulations.

32
The categories of this paper are: atom-ph
13
The categories of this paper are: quant-ph
7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
29
The categories of this paper are: funct-an
33
The categories of this paper are: plasm-ph
36
The categories of this paper are: cmp-lg
21
The categories of this paper are: adap-org
25
The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an