19 citations · 28 across the 5 of their papers we have counts for
5 papers
VFL-RPS: Relevant Participant Selection in Vertical Federated Learning
Afsana Khan, Marijn ten Thij, Guangzhi Tang +1
Federated Learning (FL) allows collaboration between different parties, while ensuring that the data across these parties is not shared. However, not every collaboration is helpful…
Incentive Allocation in Vertical Federated Learning Based on Bankruptcy Problem
Afsana Khan, Marijn ten Thij, Frank Thuijsman +1
Vertical federated learning (VFL) is a promising approach for collaboratively training machine learning models using private data partitioned vertically across different parties. I…
Vertical Federated Learning: A Structured Literature Review
Afsana Khan, Marijn ten Thij, Anna Wilbik
Federated Learning (FL) has emerged as a promising distributed learning paradigm with an added advantage of data privacy. With the growing interest in having collaboration among da…
Towards Federated Clustering: A Federated Fuzzy -Means Algorithm (FFCM)
Morris Stallmann, Anna Wilbik
Federated Learning (FL) is a setting where multiple parties with distributed data collaborate in training a joint Machine Learning (ML) model while keeping all data local at the pa…
FSS++ Workshop Report: Handling Uncertainty for Data Quality Management
Anna Wilbik
This report describes the results of the eSCF Awareness Workshop on Handling Uncertainty for Data Quality Management - Challenges from Transport and Supply Chain Management that wa…