Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Personalized Semi-Supervised Federated Learning for Human Activity
  Recognition   The most effective data-driven methods for human activities recognition (HAR)
are based on supervised learning applied to the continuous stream of sensors
data. However, these methods perform well on restricted sets of activities in
domains for which there is a fully labeled dataset. It is still a challenge to
cope with the intra- and inter-variability of activity execution among
different subjects in large scale real world deployment. Semi-supervised
learning approaches for HAR have been proposed to address the challenge of
acquiring the large amount of labeled data that is necessary in realistic
settings. However, their centralised architecture incurs in the scalability and
privacy problems when the process involves a large number of users. Federated
Learning (FL) is a promising paradigm to address these problems. However, the
FL methods that have been proposed for HAR assume that the participating users
can always obtain labels to train their local models. In this work, we propose
FedHAR: a novel hybrid method for HAR that combines semi-supervised and
federated learning. Indeed, FedHAR combines active learning and label
propagation to semi-automatically annotate the local streams of unlabeled
sensor data, and it relies on FL to build a global activity model in a scalable
and privacy-aware fashion. FedHAR also includes a transfer learning strategy to
personalize the global model on each user. We evaluated our method on two
public datasets, showing that FedHAR reaches recognition rates and
personalization capabilities similar to state-of-the-art FL supervised
approaches. As a major advantage, FedHAR only requires a very limited number of
annotated data to populate a pre-trained model and a small number of active
learning questions that quickly decrease while using the system, leading to an
effective and scalable solution for the data scarcity problem of HAR.

22
The categories of this paper are: chao-dyn
8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
19
The categories of this paper are: eess
32
The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
7
The categories of this paper are: hep-th
27
The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc
26
The categories of this paper are: bayes-an
29
The categories of this paper are: funct-an
6
The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg
13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th
14
The categories of this paper are: math-ph