38 citations · 195 across the 35 of their papers we have counts for
20 papers · 1 filter
A Large-Dimensional Analysis of Least-Squares Federated Learning via Generalized Total Variation Minimization
Ekkehard Schnoor, Malik Tiomoko, Alexander Jung
Federated learning over decentralized networks inherently trades off the suppression of local finite-sample noise through collaboration against the bias that collaboration introduc…
flow-based clustering and spectral clustering: a comparison
Y. SarcheshmehPour, Y. Tian, L. Zhang +1
We propose and study a novel graph clustering method for data with an intrinsic network structure. Similar to spectral clustering, we exploit an intrinsic network structure of data…
Clustered Federated Learning via Generalized Total Variation Minimization
Yasmin SarcheshmehPour, Yu Tian, Linli Zhang +1
We study optimization methods to train local (or personalized) models for decentralized collections of local datasets with an intrinsic network structure. This network structure ar…
Federated Learning From Big Data Over Networks
Y. Sarcheshmehpour, M. Leinonen, A. Jung
This paper formulates and studies a novel algorithm for federated learning from large collections of local datasets. This algorithm capitalizes on an intrinsic network structure th…
Local Graph Clustering with Network Lasso
Alexander Jung, Yasmin SarcheshmehPour
We study the statistical and computational properties of a network Lasso method for local graph clustering. The clusters delivered by nLasso can be characterized elegantly via netw…
An Information-Theoretic Approach to Personalized Explainable Machine Learning
Alexander Jung, Pedro H. J. Nardelli
Automated decision making is used routinely throughout our everyday life. Recommender systems decide which jobs, movies, or other user profiles might be interesting to us. Spell ch…