no code implementations • 19 Sep 2023 • Sadi Alawadi, Addi Ait-Mlouk, Salman Toor, Andreas Hellander
In this paper, we propose and evaluate a FL strategy inspired by transfer learning in order to reduce resource utilization on devices, as well as the load on the server and network in each global training round.
no code implementations • 4 Apr 2023 • Addi Ait-Mlouk, Sadi Alawadi, Salman Toor, Andreas Hellander
The POC combines Deep Bidirectional Transformer models and federated learning algorithms to protect customer data privacy during collaborative model training.
no code implementations • 23 Jan 2023 • Li Ju, Tianru Zhang, Salman Toor, Andreas Hellander
This is known as the fairness problem in federated learning.
1 code implementation • 9 Feb 2022 • Addi Ait-Mlouk, Sadi Alawadi, Salman Toor, Andreas Hellander
In addition, we present the architecture and implementation of the system, as well as provide a reference evaluation based on the SQUAD dataset, to showcase how it overcomes data privacy issues and enables knowledge sharing between alliance members in a Federated learning setting.
2 code implementations • 27 Feb 2021 • Morgan Ekmefjord, Addi Ait-Mlouk, Sadi Alawadi, Mattias Åkesson, Prashant Singh, Ola Spjuth, Salman Toor, Andreas Hellander
Federated machine learning has great promise to overcome the input privacy challenge in machine learning.
no code implementations • 12 Feb 2021 • Fredrik Wrede, Robin Eriksson, Richard Jiang, Linda Petzold, Stefan Engblom, Andreas Hellander, Prashant Singh
State-of-the-art neural network-based methods for learning summary statistics have delivered promising results for simulation-based likelihood-free parameter inference.
no code implementations • 31 Jan 2020 • Mattias Åkesson, Prashant Singh, Fredrik Wrede, Andreas Hellander
The proposed approach is demonstrated on two benchmark problem and one challenging inference problem learning parameters in a high-dimensional stochastic genetic oscillator.
no code implementations • 20 Jul 2018 • Ben Blamey, Andreas Hellander, Salman Toor
Studies have demonstrated that Apache Spark, Flink and related frameworks can perform stream processing at very high frequencies, whilst tending to focus on small messages with a computationally light `map' stage for each message; a common enterprise use case.
Distributed, Parallel, and Cluster Computing
2 code implementations • 28 Jun 2018 • Adrien Coulier, Andreas Hellander
By the use of operator-splitting we decouple the simulation of reaction-diffusion kinetics inside the cells from the simulation of molecular cell-cell interactions occurring on the boundaries between cells.
no code implementations • 22 May 2018 • Prashant Singh, Andreas Hellander
This allows approximate Bayesian computation rejection sampling to dynamically focus on a distribution over well performing summary statistics as opposed to a fixed set of statistics.