2 citations · 2 across the 4 of their papers we have counts for
4 papers
FLAM: Evaluating Model Performance with Aggregatable Measures in Federated Learning
Fabian Stricker, Jose A. Peregrina, David Bermbach +1
Performance evaluation is essential for assessing the quality of machine learning (ML) models and guiding deployment decisions. In federated learning (FL), assessing the performanc…
Revealing the influence of participant failures on model quality in cross-silo Federated Learning
Fabian Stricker, David Bermbach, Christian Zirpins
Federated Learning (FL) is a paradigm for training machine learning (ML) models in collaborative settings while preserving participants' privacy by keeping raw data local. A key re…
Analyzing the Impact of Participant Failures in Cross-Silo Federated Learning
Fabian Stricker, David Bermbach, Christian Zirpins
Federated learning (FL) is a new paradigm for training machine learning (ML) models without sharing data. While applying FL in cross-silo scenarios, where organizations collaborate…
FL-APU: A Software Architecture to Ease Practical Implementation of Cross-Silo Federated Learning
F. Stricker, J. A. Peregrina, D. Bermbach +1
Federated Learning (FL) is an upcoming technology that is increasingly applied in real-world applications. Early applications focused on cross-device scenarios, where many particip…