32 citations · 115 across the 29 of their papers we have counts for
6 papers · 1 filter
Adaptive Active Inference Agents for Heterogeneous and Lifelong Federated Learning
Anastasiya Danilenka, Alireza Furutanpey, Victor Casamayor Pujol +5
Handling heterogeneity and unpredictability are two core problems in pervasive computing. The challenge is to seamlessly integrate devices with varying computational resources in a…
Introducing Federated Learning into Internet of Things ecosystems -- preliminary considerations
Karolina Bogacka, Katarzyna Wasielewska-Michniewska, Marcin Paprzycki +4
Federated learning (FL) was proposed to facilitate the training of models in a distributed environment. It supports the protection of (local) data privacy and uses local resources…
StatMix: Data augmentation method that relies on image statistics in federated learning
Dominik Lewy, Jacek Mańdziuk, Maria Ganzha +1
Availability of large amount of annotated data is one of the pillars of deep learning success. Although numerous big datasets have been made available for research, this is often n…
Using adversarial images to improve outcomes of federated learning for non-IID data
Anastasiya Danilenka, Maria Ganzha, Marcin Paprzycki +1
One of the important problems in federated learning is how to deal with unbalanced data. This contribution introduces a novel technique designed to deal with label skewed non-IID d…
Applying machine learning to predict behavior of bus transport in Warsaw, Poland
Łukasz Pałys, Maria Ganzha, Marcin Paprzycki
Nowadays, it is possible to collect precise data describing movements of public transport. Specifically, for each bus (or tram) geoposition data can be regularly collected. This in…
Practical Aspects of Zero-Shot Learning
Elie Saad, Marcin Paprzycki, Maria Ganzha
One of important areas of machine learning research is zero-shot learning. It is applied when properly labeled training data set is not available. A number of zero-shot algorithms…