3 citations · 9 across the 9 of their papers we have counts for
9 papers
IR3DE: A Linear Router for Large Language Models
Eros Fanì, Oğuzhan Ersoy
Foundational Large Language Models (LLMs) demonstrate proficiency on a wide range of general tasks, and achieve remarkable results on various specialized tasks via domain-expert LL…
Training-Free Dynamic Upcycling of Expert Language Models
Eros Fanì, Oğuzhan Ersoy
Large Language Models (LLMs) have achieved remarkable performance on a wide range of specialized tasks, exhibiting strong problem-solving capabilities. However, training these mode…
FedDiverse: Tackling Data Heterogeneity in Federated Learning with Diversity-Driven Client Selection
Gergely D. Németh, Eros Fanì, Yeat Jeng Ng +4
Federated Learning (FL) enables decentralized training of machine learning models on distributed data while preserving privacy. However, in real-world FL settings, client data is o…
Interaction-Aware Gaussian Weighting for Clustered Federated Learning
Alessandro Licciardi, Davide Leo, Eros Fanì +2
Federated Learning (FL) emerged as a decentralized paradigm to train models while preserving privacy. However, conventional FL struggles with data heterogeneity and class imbalance…
Accelerating Heterogeneous Federated Learning with Closed-form Classifiers
Eros Fanì, Raffaello Camoriano, Barbara Caputo +1
Federated Learning (FL) methods often struggle in highly statistically heterogeneous settings. Indeed, non-IID data distributions cause client drift and biased local solutions, par…
Collaborative Visual Place Recognition through Federated Learning
Mattia Dutto, Gabriele Berton, Debora Caldarola +3
Visual Place Recognition (VPR) aims to estimate the location of an image by treating it as a retrieval problem. VPR uses a database of geo-tagged images and leverages deep neural n…
FedDrive v2: an Analysis of the Impact of Label Skewness in Federated Semantic Segmentation for Autonomous Driving
Eros Fanì, Marco Ciccone, Barbara Caputo
We propose FedDrive v2, an extension of the Federated Learning benchmark for Semantic Segmentation in Autonomous Driving. While the first version aims at studying the effect of dom…
Learning Across Domains and Devices: Style-Driven Source-Free Domain Adaptation in Clustered Federated Learning
Donald Shenaj, Eros Fanì, Marco Toldo +6
Federated Learning (FL) has recently emerged as a possible way to tackle the domain shift in real-world Semantic Segmentation (SS) without compromising the private nature of the co…
FedDrive: Generalizing Federated Learning to Semantic Segmentation in Autonomous Driving
Lidia Fantauzzo, Eros Fanì, Debora Caldarola +4
Semantic Segmentation is essential to make self-driving vehicles autonomous, enabling them to understand their surroundings by assigning individual pixels to known categories. Howe…