on-device-learning

Here are 5 public repositories matching this topic...

LauzHack / deep-learning-bootcamp

LauzHack Deep Learning Bootcamp

natural-language-processing computer-vision deep-learning audio-processing deeprl explainable-ai efficient-deep-learning on-device-learning

Updated Jul 19, 2025
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karagultm / on-device-learning

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On-Device Learning for Human Activity Recognition on Low-Power Microcontrollers

har cnn personalization human-activity-recognition on-device federated-learning 1d-cnn fedavg on-device-learning

Updated Sep 11, 2025
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Thanos3G / Federated-Tinyml-for-Urban-Sound-Classification

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Code for "A Federated Approach for Adaptive Urban Sound Classification on TinyML Edge Devices" (Sensors 2026, 26, 2854). Federated on-device learning on ESP32, a 231-parameter head trains locally and syncs over MQTT at 1.2 kB per round.

mqtt esp32 wsn smart-city acoustic-monitoring sound-classification federated-learning edge-ai tinyml edgeimpulse on-device-learning

Updated Sep 1, 2026
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abdulsamie10 / anvil

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An anomaly detector that trains on the machine it watches. Unsupervised on-device learning in fixed point on an ESP32 - no labels, no cloud, no pre-trained fault classes.

embedded esp32 anomaly-detection predictive-maintenance edge-ai on-device-learning

Updated Sep 2, 2026
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Ankan-Das / AegisFL

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AegisFL is a cloud-native, privacy-preserving federated learning platform. It uses TensorFlow Federated, Differential Privacy, and Secure Aggregation to train models across decentralized clients, ensuring HIPAA/GDPR compliance with cost-optimized Kubernetes deployment and real-time monitoring.

python machine-learning deep-learning simulation tensorflow differential-privacy edge-computing distributed-training emnist federated-learning privacy-preserving-ml tensorflow-federated on-device-learning

Updated Aug 25, 2025
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