Zobrazeno 1 - 10
of 192
pro vyhledávání: '"Cucinotta Tommaso"'
Cloud Computing has established itself as an efficient and cost-effective paradigm for the execution of web-based applications, and scientific workloads, that need elasticity and on-demand scalability capabilities. However, the evaluation of novel re
Externí odkaz:
http://arxiv.org/abs/2408.13386
The potential of transformer-based LLMs risks being hindered by privacy concerns due to their reliance on extensive datasets, possibly including sensitive information. Regulatory measures like GDPR and CCPA call for using robust auditing tools to add
Externí odkaz:
http://arxiv.org/abs/2406.16565
Autor:
Mazzola, Sergio, Ara, Gabriele, Benz, Thomas, Forsberg, Björn, Cucinotta, Tommaso, Benini, Luca
In the current high-performance and embedded computing era, full-stack energy-centric design is paramount. Use cases require increasingly high performance at an affordable power budget, often under real-time constraints. Extreme heterogeneity and par
Externí odkaz:
http://arxiv.org/abs/2401.01826
Training differentially private machine learning models requires constraining an individual's contribution to the optimization process. This is achieved by clipping the $2$-norm of their gradient at a predetermined threshold prior to averaging and ba
Externí odkaz:
http://arxiv.org/abs/2310.00829
Federated learning (FL) is a framework for training machine learning models in a distributed and collaborative manner. During training, a set of participating clients process their data stored locally, sharing only the model updates obtained by minim
Externí odkaz:
http://arxiv.org/abs/2309.00416
Federated learning (FL) is a type of collaborative machine learning where participating peers/clients process their data locally, sharing only updates to the collaborative model. This enables to build privacy-aware distributed machine learning models
Externí odkaz:
http://arxiv.org/abs/2206.03396
Autor:
Pannocchi, Luigi a, ⁎, Lahiri, Sourav b, Fichera, Silvia b, Artale, Antonino b, Cucinotta, Tommaso a
Publikováno v:
In Journal of Systems Architecture November 2024 156
Cloud auto-scaling mechanisms are typically based on reactive automation rules that scale a cluster whenever some metric, e.g., the average CPU usage among instances, exceeds a predefined threshold. Tuning these rules becomes particularly cumbersome
Externí odkaz:
http://arxiv.org/abs/2111.02133
Publikováno v:
International Conference on Intelligent and Cloud Computing, 2019
In recent years, the Internet of Things (IoT) has been growing in popularity, along with the increasingly important role played by IoT gateways, mediating the interactions among a plethora of heterogeneous IoT devices and cloud services. In this pape
Externí odkaz:
http://arxiv.org/abs/1911.08413
Publikováno v:
In Journal of Systems Architecture October 2021 119