15 citations · 17 across the 5 of their papers we have counts for
5 papers
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…
Adaptive Expert Models for Personalization in Federated Learning
Martin Isaksson, Edvin Listo Zec, Rickard Cöster +2
Federated Learning (FL) is a promising framework for distributed learning when data is private and sensitive. However, the state-of-the-art solutions in this framework are not opti…
Specialized federated learning using a mixture of experts
Edvin Listo Zec, Olof Mogren, John Martinsson +2
In federated learning, clients share a global model that has been trained on decentralized local client data. Although federated learning shows significant promise as a key approac…
Adversarial representation learning for synthetic replacement of private attributes
John Martinsson, Edvin Listo Zec, Daniel Gillblad +1
Data privacy is an increasingly important aspect of many real-world Data sources that contain sensitive information may have immense potential which could be unlocked using the rig…