GitScrider/FederatedEdgeComputing
a Federated Learning Framework adapted for resource-constrained environments, focusing on IoT devices
This framework helps you train machine learning models using data spread across many small, low-power devices like IoT sensors, without sending all that raw data to a central server. You provide local datasets on each device and a central server to coordinate, and it produces an updated, more accurate global model. It's designed for engineers and developers working with embedded systems and decentralized data.
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Use this if you need to train AI models on data from numerous resource-constrained IoT devices while maintaining data privacy and optimizing communication.
Not ideal if your data is already centralized, if you require complex deep learning models beyond ESP32 capabilities, or if you're not comfortable working with C and ESP-IDF.
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Last pushed
Oct 06, 2025
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