Exploring Federated Learning Tendencies Using a Semantic Keyword Clustering Approach
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Published:2024-06-28
Issue:7
Volume:15
Page:379
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ISSN:2078-2489
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Container-title:Information
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language:en
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Short-container-title:Information
Author:
Enguix Francisco1ORCID, Carrascosa Carlos1ORCID, Rincon Jaime2ORCID
Affiliation:
1. Valencian Research Institute for Artificial Intelligence (VRAIN), Universitat Politècnica de València (UPV), 46022 Valencia, Spain 2. Departamento de Digitalización, Escuela Politécnica Superior, Universidad de Burgos, 09006 Miranda de Ebro, Spain
Abstract
This paper presents a novel approach to analyzing trends in federated learning (FL) using automatic semantic keyword clustering. The authors collected a dataset of FL research papers from the Scopus database and extracted keywords to form a collection representing the FL research landscape. They employed natural language processing (NLP) techniques, specifically a pre-trained transformer model, to convert keywords into vector embeddings. Agglomerative clustering was then used to identify major thematic trends and sub-areas within FL. The study provides a granular view of the thematic landscape and captures the broader dynamics of research activity in FL. The key focus areas are divided into theoretical areas and practical applications of FL. The authors make their FL paper dataset and keyword clustering results publicly available. This data-driven approach moves beyond manual literature reviews and offers a comprehensive overview of the current evolution of FL.
Reference64 articles.
1. Konečný, J., McMahan, H.B., Yu, F.X., Richtárik, P., Suresh, A.T., and Bacon, D. (2016). Federated Learning: Strategies for Improving Communication Efficiency. arXiv. 2. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017, January 4–9). Attention is all you need. Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS’17, Red Hook, NY, USA. 3. Lo, K., Wang, L.L., Neumann, M., Kinney, R., and Weld, D. (2020, January 5–10). S2ORC: The Semantic Scholar Open Research Corpus. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online. 4. Word embeddings as metric recovery in semantic spaces;Hashimoto;Trans. Assoc. Comput. Linguist.,2016 5. Fredrikson, M., Jha, S., and Ristenpart, T. (2015, January 12–16). Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures. Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, CCS ’15, New York, NY, USA.
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