Achmad Imam Kistijantoro
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 synchronously. In this research, 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 distribution.
Penerapan Karya Tulis
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.