Gergely Dániel Németh

Privacy and Accuracy Implications of Model Complexity and Integration in Heterogeneous Federated Learning

2025 · IEEE Access

Federated Learning (FL) has been proposed as a privacy-preserving solution for distributed machine learning, particularly in heterogeneous FL settings where clients have varying computational capabilities and thus train models with different complexities compared to the …