Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Three Approaches for Personalization with Applications to Federated
  Learning   The standard objective in machine learning is to train a single model for all
users. However, in many learning scenarios, such as cloud computing and
federated learning, it is possible to learn a personalized model per user. In
this work, we present a systematic learning-theoretic study of personalization.
We propose and analyze three approaches: user clustering, data interpolation,
and model interpolation. For all three approaches, we provide
learning-theoretic guarantees and efficient algorithms for which we also
demonstrate the performance empirically. All of our algorithms are
model-agnostic and work for any hypothesis class.

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
2
The categories of this paper are: math
23
The categories of this paper are: solv-int
27
The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph
7
The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an
29
The categories of this paper are: funct-an
18
The categories of this paper are: econ
33
The categories of this paper are: plasm-ph
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
19
The categories of this paper are: eess
3
The categories of this paper are: physics