1 citations · 1 across the 2 of their papers we have counts for
3 papers
FeDABoost: Fairness Aware Federated Learning with Adaptive Boosting
Tharuka Kasthuri Arachchige, Veselka Boeva, Shahrooz Abghari
This work focuses on improving the performance and fairness of Federated Learning (FL) in non IID settings by enhancing model aggregation and boosting the training of underperformi…
A Method for Identification and Ranking of Requirements Sources
Eriks Klotins, Veselka Boeva, Krzysztof Wnuk +3
Requirements engineering (RE) literature acknowledges the importance of early stakeholder identification. The sources of requirements are many and also constantly changing as the m…
Hoeffding Trees with nmin adaptation
Eva García-Martín, Niklas Lavesson, Håkan Grahn +2
Machine learning software accounts for a significant amount of energy consumed in data centers. These algorithms are usually optimized towards predictive performance, i.e. accuracy…