12 citations · 21 across the 8 of their papers we have counts for
7 papers
Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions
Daniel M. Jimenez G., David Solans, Mikko Heikkila +4
Recent advances in machine learning have highlighted Federated Learning (FL) as a promising approach that enables multiple distributed users (so-called clients) to collectively tra…
PUFFLE: Balancing Privacy, Utility, and Fairness in Federated Learning
Luca Corbucci, Mikko A Heikkila, David Solans Noguero +2
Training and deploying Machine Learning models that simultaneously adhere to principles of fairness and privacy while ensuring good utility poses a significant challenge. The inter…
SenTopX: Benchmark for User Sentiment on Various Topics
Hina Qayyum, Muhammad Ikram, Benjamin Zhao +3
Toxic sentiment analysis on Twitter (X) often focuses on specific topics and events such as politics and elections. Datasets of toxic users in such research are typically gathered…
On mission Twitter Profiles: A Study of Selective Toxic Behavior
Hina Qayyum, Muhammad Ikram, Benjamin Zi Hao Zhao +3
The argument for persistent social media influence campaigns, often funded by malicious entities, is gaining traction. These entities utilize instrumented profiles to disseminate d…
Exploring the Distinctive Tweeting Patterns of Toxic Twitter Users
Hina Qayyum, Muhammad Ikram, Benjamin Zi Hao Zhao +3
In the pursuit of bolstering user safety, social media platforms deploy active moderation strategies, including content removal and user suspension. These measures target users eng…
A longitudinal study of the top 1% toxic Twitter profiles
Hina Qayyum, Benjamin Zi Hao Zhao, Ian D. Wood +3
Toxicity is endemic to online social networks including Twitter. It follows a Pareto like distribution where most of the toxicity is generated by a very small number of profiles an…