Evaluating the Effect of Volatile Federated Timeseries on Modern DNNs: Attention over Long/Short Memory

Author:

Siniosoglou Ilias1,Xouveroudis Konstantinos2,Argyriou Vasileios3,Lagkas Thomas4,Goudos Sotirios K.5,Psannis Konstantinos E.6,Sarigiannidis Panagiotis1

Affiliation:

1. University of Western Macedonia,Department of Electrical and Computer Engineering,Kozani,Greece

2. MetaMind Innovations P.C.,R&D Department,Kozani,Greece

3. Kingston University,Department of Networks and Digital Media,Kingston Upon Thames,United Kingdom

4. International Hellenic University,Department of Computer Science,Greece

5. Aritostle University of Thessaloniki,Physics Department,Thessaloniki,Greece

6. University of Macedonia,School of Information Sciences,Department of Applied Informatics,Thessaloniki,Greece

Publisher

IEEE

Reference27 articles.

1. Deep transformer models for time series forecasting: The influenza prevalence case;wu,2020

2. Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting

3. Federated learning: Strategies for improving communication efficiency;kone?n?,2016

4. Probabilistic transformer for time series analysis;tang;Advances in neural information processing systems,2021

5. 25 years of time series forecasting

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