1 citations · 1 across the 7 of their papers we have counts for
8 papers
Federated Soft Clustering via Generalized Total Variation Minimization
Shamsiiat Abdurakhmanova, Alexander Jung
We study federated soft clustering over federated learning (FL) networks of devices that each hold a private local dataset and fit a personalized Gaussian mixture model (GMM). Gene…
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…
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…
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…
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…
Enforcing Fundamental Relations via Adversarial Attacks on Input Parameter Correlations
Timo Saala, Lucie Flek, Alexander Jung +5
Correlations between input parameters play a crucial role in many scientific classification tasks, since these are often related to fundamental laws of nature. For example, in high…
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…
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…