Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Giaretta, Lodovico"'
In smart mobility, large networks of geographically distributed sensors produce vast amounts of high-frequency spatio-temporal data that must be processed in real time to avoid major disruptions. Traditional centralized approaches are increasingly un
Externí odkaz:
http://arxiv.org/abs/2412.03188
Federated Word2Vec: Leveraging Federated Learning to Encourage Collaborative Representation Learning
Large scale contextual representation models have significantly advanced NLP in recent years, understanding the semantics of text to a degree never seen before. However, they need to process large amounts of data to achieve high-quality results. Join
Externí odkaz:
http://arxiv.org/abs/2105.00831
Autor:
Giaretta, Lodovico
Recent years have seen a sharp increase in the ubiquity and power of connected devices, such as smartphones, smart appliances and smart sensors. These de- vices produce large amounts of data that can be extremely precious for training larger, more ad
Externí odkaz:
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-253794
In existing literature, GNN training has been performed mostly in centralized, and sometimes federated, settings. In this work, we consider a fully-decentralized data-private scenario, where each node has limited knowledge of the surrounding graph. W
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0b2275a995ffd571f89898fc40983165
This is an extended version of our work in [16]. In this paper, we introduce two novel algorithms to collaboratively train Naive Bayes models across multiple private data sources: Federated Naive Bayes and Gossip Naive Bayes. Instead of directly prov
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::033d332d95dfa63e7870889bcc4d9717
Autor:
Giaretta, Lodovico, Lekssays, Ahmed, Carminati, Barbara, Ferrari, Elena, Girdzijauskas, Sarunas
Early-stage detection of botnets during their spreading phase, before any attack, is fundamental to IoT security. Recently introduced lightweight memory networks represent the state of the art in this domain. However, they require a central system to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7d34afc87aab6bf1839745cf21b5e3a0
Autor:
Giaretta, Lodovico
Current Machine Learning (ML) approaches typically present either a centralized or federated architecture. However, these architectures cannot easily keep up with some of the challenges introduced by recent trends, such as the growth in the number of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::14c9432cd16cf78d7d0ab84fb70b795f
One big challenge for Industry 4.0 is leveraging the large amount of data that remain unused after collection. A variety of commercial data marketplaces have emerged in recent years to tackle this task. Despite their different business models and tar
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0d34b1b2847ed4dd417a90324000ccc9
https://zenodo.org/record/8058396
https://zenodo.org/record/8058396
Publikováno v:
Proceedings of the 19th International Conference on Security and Cryptography
Growing privacy concerns regarding personal data disclosure are contrasting with the constant need of such information for data-driven applications. To address this issue, the combination of federated learning and differential privacy is now well-est
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7167cb054a6e4acfc1bde825ec756185
Advanced NLP models require huge amounts of data from various domains to produce high-quality representations. It is useful then for a few large public and private organizations to join their corpora during training. However, factors such as legislat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::585299eaafd21a635ea0695e3e7129bf
https://doi.org/10.5281/zenodo.4679361
https://doi.org/10.5281/zenodo.4679361