STUDY OF ASYNCHRONOUS AGGREGATION ALGORITHM ON FEDERATED LEARNING SYSTEM
As the use of machine learning techniques are becoming more widespread, the need for more elaborate dataset is becoming more prevalent. This is usually done with data collection methods that pay little to no attention to the data owner's privacy and consent. Federated learning is an approach...
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| Main Author: | |
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/48518 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | As the use of machine learning techniques are becoming more widespread, the
need for more elaborate dataset is becoming more prevalent. This is usually done
with data collection methods that pay little to no attention to the data owner's
privacy and consent. Federated learning is an approach that tries to solve this
problem, where such system can train a machine learning model without centrally
storing the needed data. But one weakness of the current implementation is that
they have a slow convergence time, despite the fact that they distribute the task on
many nodes. This is mainly caused by the synchronous nature of the current
algorithm. In this paper, we test and observe the effect of asynchronous
aggregation algorithm in a federated learning setting by adapting the Stale
Synchronous Parallel algorithm. We found that asynchronous aggregation
algorithm improves convergence time in a federated learning system that has large
inequality in server-wise update frequency and has a relatively balanced data
distributio |
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