7 papers
Federated k-Means over Networks
Xu Yang, Salvatore Rastelli, Alexander Jung
We study federated clustering, where interconnected devices collaboratively cluster the data points of private local datasets. Focusing on hard clustering via the k-means principle…
Concept activation vectors: a unifying view and adversarial attacks
Ekkehard Schnoor, Malik Tiomoko, Jawher Said +2
Concept Activation Vectors (CAVs) are a tool from explainable AI, offering a promising approach for understanding how human-understandable concepts are encoded in a model's latent…
Graph-Regularized Learning of Gaussian Mixture Models
Shamsiiat Abdurakhmanova, Alex Jung
We present a graph-regularized learning of Gaussian Mixture Models (GMMs) in distributed settings with heterogeneous and limited local data. The method exploits a provided similari…
Federated Learning: From Theory to Practice
A. Jung
This book offers a hands-on introduction to building and understanding federated learning (FL) systems. FL enables multiple devices -- such as smartphones, sensors, or local comput…
Plug In and Learn: Federated Intelligence over a Smart Grid of Models
S. Abdurakhmanova, Y. SarcheshmehPour, A. Jung
We present a model-agnostic federated learning method that mirrors the operation of a smart power grid: diverse local models, like energy prosumers, train independently on their ow…
Your Data, My Model: Learning Who Really Helps in Federated Learning
Shamsiiat Abdurakhmanova, Amirhossein Mohammadi, Yasmin SarcheshmehPour +1
Many important machine learning applications involve networks of devices-such as wearables or smartphones-that generate local data and train personalized models. A key challenge is…