1 citations · 1 across the 5 of their papers we have counts for
5 papers
Subgraph Federated Learning via Spectral Methods
Javad Aliakbari, Johan Östman, Ashkan Panahi +1
We consider the problem of federated learning (FL) with graph-structured data distributed across multiple clients. In particular, we address the prevalent scenario of interconnecte…
Privacy Risks in Time Series Forecasting: User- and Record-Level Membership Inference
Nicolas Johansson, Tobias Olsson, Daniel Nilsson +2
Membership inference attacks (MIAs) aim to determine whether specific data were used to train a model. While extensively studied on classification models, their impact on time seri…
AMLgentex: Mobilizing Data-Driven Research to Combat Money Laundering
Johan Östman, Edvin Callisen, Anton Chen +7
Money laundering enables organized crime by moving illicit funds into the legitimate economy. Although trillions of dollars are laundered each year, detection rates remain low beca…
Soft-Decision Decoding for LDPC Code-Based Quantitative Group Testing
Marvin Xhemrishi, Johan Östman, Alexandre Graell i Amat
We consider the problem of identifying defective items in a population with non-adaptive quantitative group testing. For this scenario, Mashauri et al. recently proposed a low-dens…
Poisoning Attacks on Federated Learning for Autonomous Driving
Sonakshi Garg, Hugo Jönsson, Gustav Kalander +4
Federated Learning (FL) is a decentralized learning paradigm, enabling parties to collaboratively train models while keeping their data confidential. Within autonomous driving, it…