27 citations · 59 across the 14 of their papers we have counts for
12 papers · 1 filter
Federated Learning with Heterogeneous and Private Label Sets
Adam Breitholtz, Edvin Listo Zec, Fredrik D. Johansson
Although common in real-world applications, heterogeneous client label sets are rarely investigated in federated learning (FL). Furthermore, in the cases they are, clients are assu…
Overcoming label shift with target-aware federated learning
Edvin Listo Zec, Adam Breitholtz, Fredrik D. Johansson
Federated learning enables multiple actors to collaboratively train models without sharing private data. Existing algorithms are successful and well-justified in this task when the…
On the effects of similarity metrics in decentralized deep learning under distributional shift
Edvin Listo Zec, Tom Hagander, Eric Ihre-Thomason +1
Decentralized Learning (DL) enables privacy-preserving collaboration among organizations or users to enhance the performance of local deep learning models. However, model aggregati…
Concept-aware clustering for decentralized deep learning under temporal shift
Marcus Toftås, Emilie Klefbom, Edvin Listo Zec +2
Decentralized deep learning requires dealing with non-iid data across clients, which may also change over time due to temporal shifts. While non-iid data has been extensively studi…
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…
EFFGAN: Ensembles of fine-tuned federated GANs
Ebba Ekblom, Edvin Listo Zec, Olof Mogren
Generative adversarial networks have proven to be a powerful tool for learning complex and high-dimensional data distributions, but issues such as mode collapse have been shown to…
Decentralized adaptive clustering of deep nets is beneficial for client collaboration
Edvin Listo Zec, Ebba Ekblom, Martin Willbo +2
We study the problem of training personalized deep learning models in a decentralized peer-to-peer setting, focusing on the setting where data distributions differ between the clie…
Decentralized federated learning of deep neural networks on non-iid data
Noa Onoszko, Gustav Karlsson, Olof Mogren +1
We tackle the non-convex problem of learning a personalized deep learning model in a decentralized setting. More specifically, we study decentralized federated learning, a peer-to-…
Scaling Federated Learning for Fine-tuning of Large Language Models
Agrin Hilmkil, Sebastian Callh, Matteo Barbieri +3
Federated learning (FL) is a promising approach to distributed compute, as well as distributed data, and provides a level of privacy and compliance to legal frameworks. This makes…
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…