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cs.DC2026
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…
cs.DC2025
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…
cs.DC2025
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…