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Efficient and LightWeight Federated Learning via Asynchronous Distributed DropoutChen Dun Rice UniversityMirian Hipolito Microsoft ResearchChris Jermaine Rice UniversityAbstract Asynchronous learning
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01
Identify the data sources that will participate in the federated system.
02
Establish a clear communication protocol between nodes to ensure data can be shared securely.
03
Choose a lightweight framework that supports federated learning and ensures minimal resource overhead.
04
Define the model architecture that will be trained in a federated manner.
05
Implement data preprocessing steps to ensure consistency across different data sources.
06
Set up a mechanism for aggregating model updates from different nodes while preserving individual data privacy.
07
Test the federated setup with a small scale simulation before scaling up.
08
Monitor performance metrics and resource usage continuously to ensure efficiency.

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01
Organizations with distributed data sources looking to improve machine learning models while maintaining data privacy.
02
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Research institutions seeking to analyze data across different locations without transferring sensitive datasets.
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Tech companies developing products that require real-time learning from user devices without centralizing the data.
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