FeatureCloud Hackathon
“The best way to predict the future is to invent it.” - Alan Kay
Introduction
In June 2022, I participated remotely in the FeatureCloud Hackathon, which aimed at exploring advanced applications of Federated Learning in metagenome research. This project focused on enhancing data privacy and computational efficiency using cloud computing.
Progress
Research and Planning
The initial phase involved understanding the current challenges and limitations in metagenome research, particularly those related to data privacy. This helped in planning the implementation of Federated Learning solutions. The featurecloud platform was used to access the necessary resources and tools, since they were organizing the hackathon.
Data Preparation
The metagenome data was gained through the featurecloud platform by harnessing the Federated Learning framework. This data was then preprocessed and cleaned to ensure that it was ready for the machine learning tasks.
Federated Learning Implementation
The Federated Learning model was developed using the PySyft library, which allowed for decentralized training of the machine learning model. This model was then deployed on the featurecloud platform to leverage its computational resources.
Results
The prototype successfully demonstrated the feasibility of using Federated Learning for metagenome analysis. It showed how privacy can be maintained while still leveraging the computational power of cloud resources. View more about the project results and contributions here.
Technology Learned/Used
Throughout the hackathon, I deepened my understanding and skills in:
- Python for programming
- Federated Learning frameworks for decentralized machine learning
Conclusion
The FeatureCloud Hackathon was a significant learning opportunity that highlighted the potential of Federated Learning in sensitive research areas like genomics. The gained experience in cloud computing and Federated Learning was useful in my future projects and research endeavors.
