7 papers
A Full Compression Pipeline for Green Federated Learning in Communication-Constrained Environments
Elouan Colybes, Shirin Salehi, Anke Schmeink
Federated Learning (FL) enables collaborative model training across distributed clients without sharing raw data, thereby preserving privacy. However, FL often suffers from signifi…
FedSCS-XGB -- Federated Server-centric surrogate XGBoost for continual health monitoring
Felix Walger, Mehdi Ejtehadi, Anke Schmeink +1
Wearable sensors with local data processing can detect health threats early, enhance documentation, and support personalized therapy. In the context of spinal cord injury (SCI), wh…
Parallel Split Learning with Global Sampling
Mohammad Kohankhaki, Ahmad Ayad, Mahdi Barhoush +1
Parallel split learning (PSL) suffers from two intertwined issues: the effective batch size grows with the number of clients, and data that is not identically and independently dis…
Active Learning Using Aggregated Acquisition Functions: Accuracy and Sustainability Analysis
Cédric Jung, Shirin Salehi, Anke Schmeink
Active learning (AL) is a machine learning (ML) approach that strategically selects the most informative samples for annotation during training, aiming to minimize annotation costs…
Study of Robust Power Allocation for User-Centric Cell-Free Massive MIMO Networks
Saeed Mashdour, Saeed Mohammadzadeh, André R. Flores +3
In cell-free massive multiple-input multiple-output (MIMO) networks, robust resource allocation is critical to ensure reliable system performance in the presence of channel uncerta…
Machine Learning-Based AP Selection in User-Centric Cell-free Multiple-Antenna Networks
S. Salehi, S. Mashdour, O. Tamyigit +4
User-centric cell-free (UCCF) massive multiple-input multiple-output (MIMO) systems are considered a viable solution to realize the advantages offered by cell-free (CF) networks, i…