21 citations · 32 across the 8 of their papers we have counts for
9 papers
Towards Holistic Disease Risk Prediction using Small Language Models
Liv Björkdahl, Oskar Pauli, Johan Östman +3
Data in the healthcare domain arise from a variety of sources and modalities, such as x-ray images, continuous measurements, and clinical notes. Medical practitioners integrate the…
Decoupled Subgraph Federated Learning
Javad Aliakbari, Johan Östman, Alexandre Graell i Amat
We address the challenge of federated learning on graph-structured data distributed across multiple clients. Specifically, we focus on the prevalent scenario of interconnected subg…
Decentralised Semi-supervised Onboard Learning for Scene Classification in Low-Earth Orbit
Johan Östman, Pablo Gomez, Vinutha Magal Shreenath +1
Onboard machine learning on the latest satellite hardware offers the potential for significant savings in communication and operational costs. We showcase the training of a machine…
FedGT: Identification of Malicious Clients in Federated Learning with Secure Aggregation
Marvin Xhemrishi, Johan Östman, Antonia Wachter-Zeh +1
We propose FedGT, a novel framework for identifying malicious clients in federated learning with secure aggregation. Inspired by group testing, the framework leverages overlapping…
PAseos Simulates the Environment for Operating multiple Spacecraft
Pablo Gómez, Johan Östman, Vinutha Magal Shreenath +1
The next generation of spacecraft is anticipated to enable various new applications involving onboard processing, machine learning and decentralised operational scenarios. Even tho…
Decentralized Online Bandit Optimization on Directed Graphs with Regret Bounds
Johan Östman, Ather Gattami, Daniel Gillblad
We consider a decentralized multiplayer game, played over rounds, with a leader-follower hierarchy described by a directed acyclic graph. For each round, the graph structure di…
Efficient Node Selection in Private Personalized Decentralized Learning
Edvin Listo Zec, Johan Östman, Olof Mogren +1
Personalized decentralized learning is a promising paradigm for distributed learning, enabling each node to train a local model on its own data and collaborate with other nodes to…
URLLC with Massive MIMO: Analysis and Design at Finite Blocklength
Johan Östman, Alejandro Lancho, Giuseppe Durisi +1
The fast adoption of Massive MIMO for high-throughput communications was enabled by many research contributions mostly relying on infinite-blocklength information-theoretic bounds.…
Low-latency Ultra Reliable 5G Communications: Finite-Blocklength Bounds and Coding Schemes
Johan Östman, Giuseppe Durisi, Erik G. Ström +3
Future autonomous systems require wireless connectivity able to support extremely stringent requirements on both latency and reliability. In this paper, we leverage recent developm…