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Maximilian Andreas Hoefler

2 papers indexed

arxivcs.LGcs.AI2026-07-08

Collaborative Synthetic Data Generation for Knowledge Transfer in Federated Learning

Maximilian Andreas Hoefler, Karsten Mueller, Wojciech Samek

One-shot federated learning (OSFL) addresses the communication overhead of federated learning by limiting training to a single round, but doing so without sacrificing model quality is non-trivial, particularly when client data distributions diverge. Recent work has addressed this…

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arxivcs.LGcs.AI2026-06-30

FedXDS: Leveraging Model Attribution Methods to counteract Data Heterogeneity in Federated Learning

Maximilian Andreas Hoefler, Karsten Mueller, Wojciech Samek

Explainable AI (XAI) methods have demonstrated significant success in recent years at identifying relevant features in input data that drive deep learning model decisions, enhancing interpretability for users. However, the potential of XAI beyond providing model transparency has…

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