Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Personalized Federated Learning Algorithm: an Application in Anomaly
  Detection   Federated Learning (FL) has recently emerged as a promising method that
employs a distributed learning model structure to overcome data privacy and
transmission issues paused by central machine learning models. In FL, datasets
collected from different devices or sensors are used to train local models
(clients) each of which shares its learning with a centralized model (server).
However, this distributed learning approach presents unique learning challenges
as the data used at local clients can be non-IID (Independent and Identically
Distributed) and statistically diverse which decrease learning accuracy in the
central model. In this paper, we overcome this problem by proposing a novel
Personalized Conditional FedAvg (PC-FedAvg) which aims to control weights
communication and aggregation augmented with a tailored learning algorithm to
personalize the resulting models at each client. Our experimental validation on
two datasets showed that our PC-FedAvg precisely constructed generalized
clients' models and thus achieved higher accuracy compared to other
state-of-the-art methods.

24
The categories of this paper are: dg-ga
6
The categories of this paper are: astro-ph
18
The categories of this paper are: econ
8
The categories of this paper are: hep-ex
16
The categories of this paper are: stat
14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
25
The categories of this paper are: patt-sol
31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn
4
The categories of this paper are: cond-mat
7
The categories of this paper are: hep-th
21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
29
The categories of this paper are: funct-an
34
The categories of this paper are: chem-ph