1 code implementation • 27 Mar 2024 • Sabrina Herbst, Vincenzo De Maio, Ivona Brandic
The increasing capabilities of Machine Learning (ML) models go hand in hand with an immense amount of data and computational power required for training.
no code implementations • 25 Mar 2024 • Paul Joe Maliakel, Shashikant Ilager, Ivona Brandic
Federated Learning (FL) provides a privacy-preserving mechanism for distributed training of machine learning models on networked devices (e. g., mobile devices, IoT edge nodes).
no code implementations • 23 Feb 2024 • Sabrina Herbst, Vincenzo De Maio, Ivona Brandic
However, challenges such as (1) the encoding of data from the classical to the quantum domain, (2) hyperparameter tuning, and (3) the integration of quantum hardware into a distributed computing continuum limit the adoption of quantum machine learning for urgent analytics.
no code implementations • 6 Sep 2023 • Daniel Hofstätter, Shashikant Ilager, Ivan Lujic, Ivona Brandic
Symbolic Representation (SR) algorithms are promising solutions to reduce the data size by converting actual raw data into symbols.
no code implementations • 22 Nov 2021 • Hao Zhou, Atakan Aral, Ivona Brandic, Melike Erol-Kantarci
Microgrids (MGs) are important players for the future transactive energy systems where a number of intelligent Internet of Things (IoT) devices interact for energy management in the smart grid.
no code implementations • 27 Apr 2020 • Mahdi Bohlouli, Frank Schulz, Lefteris Angelis, David Pahor, Ivona Brandic, David Atlan, Rosemary Tate
This paper presents the vision of an integrated plat-form for big data analysis that combines all these aspects.