Federated Multi-Task Learning on Acoustic Signals for Predictive Maintenance
Author
Term
4. semester
Education
Publication year
2021
Submitted on
2021-05-31
Pages
95
Abstract
This Master's thesis investigates the possibility of developing a federated multi-task learning algorithm with focus on minimal computational complexity and low communications requirements, for use in a distributed predictive maintenance setting. This Master's thesis is a collaboration with Grundfos A/S on predictive maintenance on acoustic data, from their pumping systems. The proposed solution - developed in this work - is compared to the current state-of-the-art federated multi-task algorithm, the MOCHA algorithm. We compare the two solution using ROC curves, which yielded an average decreased performance, in terms of AUC, for 100 runs of each algorithms, across 9 tasks, of 0.095. Though the proposed solution performs worse, the computational complexity is about 150 times less, in terms of FLOPs, compared to that of the MOCHA algorithm.
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