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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id-itb.:485182020-06-29T19:36:46ZSTUDY OF ASYNCHRONOUS AGGREGATION ALGORITHM ON FEDERATED LEARNING SYSTEM Sang Diwangkara, Senapati Indonesia Final Project asynchronous, non-iid, federated learning, machine learning, distributed system, distributed training INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/48518 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 text |
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Indonesia Indonesia |
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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 |
| format |
Final Project |
| author |
Sang Diwangkara, Senapati |
| spellingShingle |
Sang Diwangkara, Senapati STUDY OF ASYNCHRONOUS AGGREGATION ALGORITHM ON FEDERATED LEARNING SYSTEM |
| author_facet |
Sang Diwangkara, Senapati |
| author_sort |
Sang Diwangkara, Senapati |
| title |
STUDY OF ASYNCHRONOUS AGGREGATION ALGORITHM ON FEDERATED LEARNING SYSTEM |
| title_short |
STUDY OF ASYNCHRONOUS AGGREGATION ALGORITHM ON FEDERATED LEARNING SYSTEM |
| title_full |
STUDY OF ASYNCHRONOUS AGGREGATION ALGORITHM ON FEDERATED LEARNING SYSTEM |
| title_fullStr |
STUDY OF ASYNCHRONOUS AGGREGATION ALGORITHM ON FEDERATED LEARNING SYSTEM |
| title_full_unstemmed |
STUDY OF ASYNCHRONOUS AGGREGATION ALGORITHM ON FEDERATED LEARNING SYSTEM |
| title_sort |
study of asynchronous aggregation algorithm on federated learning system |
| url |
https://digilib.itb.ac.id/gdl/view/48518 |
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1823641524575928320 |