omarfoq/streaming-fl
Official code for "Federated Learning for Data Streams" (AISTATS'23)
This project helps machine learning practitioners and researchers develop models from continuous, real-time data generated by many distributed devices, like IoT sensors or smartphones, without ever centralizing the raw data. It takes in streams of data from various sources and produces trained machine learning models. This is ideal for those managing data privacy and device-generated information at scale.
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Use this if you need to train machine learning models on an ongoing flow of data from numerous devices while keeping that data private and localized on each device.
Not ideal if your data is static, already collected, or can be easily centralized for model training.
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Apache-2.0
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Last pushed
Jan 09, 2023
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