Joint Learning: A Pattern for Reliable and Efficient Decision-Making in Self-Adaptive Internet of Things-Reference-Cited by-同舟云学术

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Joint Learning: A Pattern for Reliable and Efficient Decision-Making in Self-Adaptive Internet of Things

Published:2023-07-05 Issue: Volume:1 Page:1-9
ISSN:
Container-title:Proceedings of the 28th European Conference on Pattern Languages of Programs
language:
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Author:

Provoost Michiel¹^ORCID,Weyns Danny²^ORCID,Van Landuyt Dimitri¹^ORCID,Michiels Sam¹^ORCID,Bureš Tomáš³^ORCID

Affiliation:

1. Department of Computer Science, Katholieke Universiteit Leuven, Belgium

2. Department of Computer Science, Katholieke Universiteit Leuven, Belgium and Department of Computer Science and Media Technology, Linnaeus University, Sweden

3. Department of Distributed and Dependable Systems, Charles University, Czech Republic

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3628034.3628043

Reference64 articles.

1. A Survey on Federated Learning: The Journey From Centralized to Distributed On-Site Learning and Beyond

2. Understanding the Limits of LoRaWAN

3. Trapit Bansal, Jakub Pachocki, Szymon Sidor, Ilya Sutskever, and Igor Mordatch. 2018. Emergent Complexity via Multi-Agent Competition. In International Conference on Learning Representations. https://openreview.net/forum?id=Sy0GnUxCb

4. Decentralised Learning in Federated Deployment Environments

5. Aurélien Bellet, Rachid Guerraoui, Mahsa Taziki, and Marc Tommasi. 2018. Personalized and private peer-to-peer machine learning. In International Conference on Artificial Intelligence and Statistics. PMLR, 473–481.

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