3 citations · 8 across the 6 of their papers we have counts for
6 papers
Jelly: a Fast and Convenient RDF Serialization Format
Piotr Sowinski, Karolina Bogacka, Anastasiya Danilenka +1
Existing RDF serialization formats such as Turtle, N-Quads, and JSON-LD are widely used for communication and storage in knowledge graph and Semantic Web applications. However, the…
Jelly-Patch: a Fast Format for Recording Changes in RDF Datasets
Piotr Sowinski, Kacper Grzymkowski, Anastasiya Danilenka
Recording data changes in RDF systems is a crucial capability, needed to support auditing, incremental backups, database replication, and event-driven workflows. In large-scale and…
Towards Adaptive Asynchronous Federated Learning for Human Activity Recognition
Rastko Gajanin, Anastasiya Danilenka, Andrea Morichetta +1
In this work, we tackle the problem of performing multi-label classification in the case of extremely heterogeneous data and with decentralized Machine Learning. Solving this issue…
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…
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…