Distributed Semi-Supervised Learning and Audio Recognition of Road Surfaces
Typ
Examensarbete för masterexamen
Program
Publicerad
2022
Författare
Davidsson, Adam
Larsson, Simon
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
The automotive industry is a promising environment for machine learning. However,
current machine learning techniques do not meet all the requirements of many possible
applications. Requirement such as privacy preservation, limited communication
and semi-supervision. To satisfy these requirements, this thesis proposes a simple
distributed semi-supervised algorithm (distributed FixMatch). Furthermore, we apply
this algorithm to a real-world problem, detecting road surface types from audio.
In applying the semi-supervised algorithm to this problem, we also propose a simple
augmentation technique for audio features. The proposed algorithm was tested on
two real datasets, where the algorithm was compared to a supervised training algorithm.
The results suggest that the algorithm successfully leveraged unlabeled data.
Furthermore, a theoretical analysis and a simulation show that the communication
cost of the proposed algorithm was lower than federated or centralized alternatives.
Beskrivning
Ämne/nyckelord
Machine learning , Federated learning , Semi-Supervised learning , Distributed learning , Audio Recognition , Road Surface detection , Neural Network