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cs.LG2026
Balancing Privacy-Quality-Efficiency in Federated Learning through Round-Based Interleaving of Protection Techniques
Yenan Wang, Carla Fabiana Chiasserini, Elad Michael Schiller
In federated learning (FL), balancing privacy protection, learning quality, and efficiency remains a challenge. Privacy protection mechanisms, such as Differential Privacy (DP), de…
cs.LG2026
Integrating Homomorphic Encryption and Synthetic Data in FL for Privacy and Learning Quality
Yenan Wang, Carla Fabiana Chiasserini, Elad Michael Schiller
Federated learning (FL) enables collaborative training of machine learning models without sharing sensitive client data, making it a cornerstone for privacy-critical applications.…