Search Results for author: Marco Aldinucci

Found 11 papers, 7 papers with code

DALLMi: Domain Adaption for LLM-based Multi-label Classifier

1 code implementation3 May 2024 Miruna Beţianu, Abele Mălan, Marco Aldinucci, Robert Birke, Lydia Chen

In this paper, we design DALLMi, Domain Adaptation Large Language Model interpolator, a first-of-its-kind semi-supervised domain adaptation method for text data models based on LLMs, specifically BERT.

Domain Adaptation Language Modelling +3

Benchmarking FedAvg and FedCurv for Image Classification Tasks

no code implementations31 Mar 2023 Bruno Casella, Roberto Esposito, Carlo Cavazzoni, Marco Aldinucci

Data carry a value that might vanish when shared with others; the ability to avoid sharing the data enables industrial applications where security and privacy are of paramount importance, making it possible to train global models by implementing only local policies which can be run independently and even on air-gapped data centres.

Benchmarking Classification +2

Experimenting with Normalization Layers in Federated Learning on non-IID scenarios

1 code implementation19 Mar 2023 Bruno Casella, Roberto Esposito, Antonio Sciarappa, Carlo Cavazzoni, Marco Aldinucci

Training Deep Learning (DL) models require large, high-quality datasets, often assembled with data from different institutions.

Federated Learning Privacy Preserving

Model-Agnostic Federated Learning

1 code implementation8 Mar 2023 Gianluca Mittone, Walter Riviera, Iacopo Colonnelli, Robert Birke, Marco Aldinucci

MAFL marries a model-agnostic FL algorithm, AdaBoost. F, with an open industry-grade FL framework: Intel OpenFL.

Federated Learning

A Federated Learning Benchmark for Drug-Target Interaction

1 code implementation15 Feb 2023 Gianluca Mittone, Filip Svoboda, Marco Aldinucci, Nicholas D. Lane, Pietro Lio

Aggregating pharmaceutical data in the drug-target interaction (DTI) domain has the potential to deliver life-saving breakthroughs.

Federated Learning Privacy Preserving

Neural Transformers for Intraductal Papillary Mucosal Neoplasms (IPMN) Classification in MRI images

1 code implementation21 Jun 2022 Federica Proietto Salanitri, Giovanni Bellitto, Simone Palazzo, Ismail Irmakci, Michael B. Wallace, Candice W. Bolan, Megan Engels, Sanne Hoogenboom, Marco Aldinucci, Ulas Bagci, Daniela Giordano, Concetto Spampinato

Early detection of precancerous cysts or neoplasms, i. e., Intraductal Papillary Mucosal Neoplasms (IPMN), in pancreas is a challenging and complex task, and it may lead to a more favourable outcome.

FedER: Federated Learning through Experience Replay and Privacy-Preserving Data Synthesis

1 code implementation20 Jun 2022 Matteo Pennisi, Federica Proietto Salanitri, Giovanni Bellitto, Bruno Casella, Marco Aldinucci, Simone Palazzo, Concetto Spampinato

In the medical field, multi-center collaborations are often sought to yield more generalizable findings by leveraging the heterogeneity of patient and clinical data.

Federated Learning Privacy Preserving

Pushing the boundaries of parallel Deep Learning -- A practical approach

no code implementations25 Jun 2018 Paolo Viviani, Maurizio Drocco, Marco Aldinucci

This work aims to assess the state of the art of data parallel deep neural network training, trying to identify potential research tracks to be exploited for performance improvement.

Deep Learning

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