Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Accurate and Fast Federated Learning via IID and Communication-Aware
  Grouping   Federated learning has emerged as a new paradigm of collaborative machine
learning; however, it has also faced several challenges such as non-independent
and identically distributed(IID) data and high communication cost. To this end,
we propose a novel framework of IID and communication-aware group federated
learning that simultaneously maximizes both accuracy and communication speed by
grouping nodes based on data distributions and physical locations of the nodes.
Furthermore, we provide a formal convergence analysis and an efficient
optimization algorithm called FedAvg-IC. Experimental results show that,
compared with the state-of-the-art algorithms, FedAvg-IC improved the test
accuracy by up to 22.2% and simultaneously reduced the communication time to as
small as 12%.

25
The categories of this paper are: patt-sol
21
The categories of this paper are: adap-org
23
The categories of this paper are: solv-int
17
The categories of this paper are: q-fin
8
The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
3
The categories of this paper are: physics
16
The categories of this paper are: stat
31
The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
7
The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
15
The categories of this paper are: nucl-ex
37
The categories of this paper are: supr-con