Online Learning and Model Pruning Against Concept Drifts in Edge Devices-Reference-Cited by-同舟云学术

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Online Learning and Model Pruning Against Concept Drifts in Edge Devices

Published:2024-06-24 Issue: Volume: Page:351-356
ISSN:
Container-title:2024 IEEE 10th International Conference on Network Softwarization (NetSoft)
language:
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Author:

Magnani Simone¹,Tirupathi Seshu²,Doriguzzi-Corin Roberto¹,Nedoshivina Liubov²,Braghin Stefano²,Siracusa Domenico¹

Affiliation:

1. Fondazione Bruno Kessler,Cybersecurity,Italy

2. IBM Research Europe,Ireland

Funder

Horizon Europe

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10587319/10588876/10588946.pdf?arnumber=10588946

Reference14 articles.

1. Machine Learning at the Network Edge: A Survey

2. Convergence of Edge Computing and Deep Learning: A Comprehensive Survey

3. Pruning Federated Learning Models for Anomaly Detection in Resource-Constrained Environments

4. Enhancing Network Intrusion Detection: An Online Approach for Performance Analysis;Magnani

5. Machine learning for streaming data

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