Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Guerra, Elia"'
Autor:
Dhasade, Akash, Dini, Paolo, Guerra, Elia, Kermarrec, Anne-Marie, Miozzo, Marco, Pires, Rafael, Sharma, Rishi, de Vos, Martijn
Decentralized learning (DL) offers a powerful framework where nodes collaboratively train models without sharing raw data and without the coordination of a central server. In the iterative rounds of DL, models are trained locally, shared with neighbo
Externí odkaz:
http://arxiv.org/abs/2407.01283
Blockchain promises to enhance distributed machine learning (ML) approaches such as federated learning (FL) by providing further decentralization, security, immutability, and trust, which are key properties for enabling collaborative intelligence in
Externí odkaz:
http://arxiv.org/abs/2310.07471
Autor:
Perifanis, Vasileios, Pavlidis, Nikolaos, Yilmaz, Selim F., Wilhelmi, Francesc, Guerra, Elia, Miozzo, Marco, Efraimidis, Pavlos S., Dini, Paolo, Koutsiamanis, Remous-Aris
Cellular traffic prediction is a crucial activity for optimizing networks in fifth-generation (5G) networks and beyond, as accurate forecasting is essential for intelligent network design, resource allocation and anomaly mitigation. Although machine
Externí odkaz:
http://arxiv.org/abs/2309.10645
Federated learning is one of the most appealing alternatives to the standard centralized learning paradigm, allowing a heterogeneous set of devices to train a machine learning model without sharing their raw data. However, it requires a central serve
Externí odkaz:
http://arxiv.org/abs/2209.07124
Federated learning (FL), thanks in part to the emergence of the edge computing paradigm, is expected to enable true real-time applications in production environments. However, its original dependence on a central server for orchestration raises sever
Externí odkaz:
http://arxiv.org/abs/2205.10201
Publikováno v:
In Computer Networks May 2024 245
Federated learning is one of the most appealing alternatives to the standard centralized learning paradigm, allowing a heterogeneous set of devices to train a machine learning model without sharing their raw data. However, it requires a central serve
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c29cd81e4a0fbd9d1e5c1afb1714903c
http://arxiv.org/abs/2209.07124
http://arxiv.org/abs/2209.07124
Publikováno v:
Repositório Institucional do FGVFundação Getulio VargasFGV.
Submitted by BKAB Setor Proc. Técnicos FGV-SP (biblioteca.sp.cat@fgv.br) on 2013-02-18T17:11:38Z No. of bitstreams: 1 1197800467.pdf: 15657953 bytes, checksum: 97479e7e6ab96361c3933f0b9f242511 (MD5)
No presente trabalho, dentro da teoria das or
No presente trabalho, dentro da teoria das or
Externí odkaz:
http://hdl.handle.net/10438/10497