35 citations · 41 across the 6 of their papers we have counts for
3 papers · 1 filter
Towards Graph-Based Privacy-Preserving Federated Learning: ModelNet -- A ResNet-based Model Classification Dataset
Abhisek Ray, Lukas Esterle
Federated Learning (FL) has emerged as a powerful paradigm for training machine learning models across distributed data sources while preserving data locality. However, the privacy…
Proximity-based Self-Federated Learning
Davide Domini, Gianluca Aguzzi, Nicolas Farabegoli +2
In recent advancements in machine learning, federated learning allows a network of distributed clients to collaboratively develop a global model without needing to share their loca…
Adaptive Parameterization of Deep Learning Models for Federated Learning
Morten From Elvebakken, Alexandros Iosifidis, Lukas Esterle
Federated Learning offers a way to train deep neural networks in a distributed fashion. While this addresses limitations related to distributed data, it incurs a communication over…