no code implementations • 2 May 2024 • Sonakshi Garg, Hugo Jönsson, Gustav Kalander, Axel Nilsson, Bhhaanu Pirange, Viktor Valadi, Johan Östman
FLStealth, an untargeted attack, aims at providing model updates that deteriorate the global model performance while appearing benign.
no code implementations • 26 Mar 2024 • Khac-Hoang Ngo, Johan Östman, Giuseppe Durisi, Alexandre Graell i Amat
In this paper, we delve into the privacy implications of SecAgg by treating it as a local differential privacy (LDP) mechanism for each local update.
no code implementations • 29 Feb 2024 • Javad Aliakbari, Johan Östman, Alexandre Graell i Amat
We address the challenge of federated learning on graph-structured data distributed across multiple clients.
no code implementations • 9 May 2023 • Marvin Xhemrishi, Johan Östman, Antonia Wachter-Zeh, Alexandre Graell i Amat
Inspired by group testing, the framework leverages overlapping groups of clients to identify the presence of malicious clients in the groups via a decoding operation.
no code implementations • 6 May 2023 • Johan Östman, Pablo Gomez, Vinutha Magal Shreenath, Gabriele Meoni
Onboard machine learning on the latest satellite hardware offers the potential for significant savings in communication and operational costs.
1 code implementation • 30 Jan 2023 • Edvin Listo Zec, Johan Östman, Olof Mogren, Daniel Gillblad
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 improve without sharing any data.
no code implementations • 27 Jan 2023 • Johan Östman, Ather Gattami, Daniel Gillblad
We consider a decentralized multiplayer game, played over $T$ rounds, with a leader-follower hierarchy described by a directed acyclic graph.