Zobrazeno 1 - 10
of 107
pro vyhledávání: '"Fodor, Viktoria"'
In this paper, we depart from the widely-used gradient descent-based hierarchical federated learning (FL) algorithms to develop a novel hierarchical FL framework based on the alternating direction method of multipliers (ADMM). Within this framework,
Externí odkaz:
http://arxiv.org/abs/2409.18796
This paper presents a novel hierarchical federated learning algorithm within multiple sets that incorporates quantization for communication-efficiency and demonstrates resilience to statistical heterogeneity. Unlike conventional hierarchical federate
Externí odkaz:
http://arxiv.org/abs/2403.01540
When implementing hierarchical federated learning over wireless networks, scalability assurance and the ability to handle both interference and device data heterogeneity are crucial. This work introduces a learning method designed to address these ch
Externí odkaz:
http://arxiv.org/abs/2401.01442
Over-the-air computation (OAC) is a promising wireless communication method for aggregating data from many devices in dense wireless networks. The fundamental idea of OAC is to exploit signal superposition to compute functions of multiple simultaneou
Externí odkaz:
http://arxiv.org/abs/2309.16033
Autor:
Peris, Jaume Anguera, Fodor, Viktoria
Edge intelligence is an emerging technology where the base stations located at the edge of the network are equipped with computing units that provide machine learning services to the end users. To provide high-quality services in a cost-efficient way
Externí odkaz:
http://arxiv.org/abs/2305.05568
When implementing hierarchical federated learning over wireless networks, scalability assurance and the ability to handle both interference and device data heterogeneity are crucial. This work introduces a new two-level learning method designed to ad
Externí odkaz:
http://arxiv.org/abs/2211.16162
Autor:
Peris, Jaume Anguera, Fodor, Viktoria
Edge intelligence is a scalable solution for analyzing distributed data, but it cannot provide reliable services in large-scale cellular networks unless the inherent aspects of fading and interference are also taken into consideration. In this paper,
Externí odkaz:
http://arxiv.org/abs/2202.08200
Motivated by increasing computational capabilities of wireless devices, as well as unprecedented levels of user- and device-generated data, new distributed machine learning (ML) methods have emerged. In the wireless community, Federated Learning (FL)
Externí odkaz:
http://arxiv.org/abs/2111.10267
Autor:
Hellström, Henrik, Silva Jr, José Mairton B. da, Amiri, Mohammad Mohammadi, Chen, Mingzhe, Fodor, Viktoria, Poor, H. Vincent, Fischione, Carlo
As data generation increasingly takes place on devices without a wired connection, machine learning (ML) related traffic will be ubiquitous in wireless networks. Many studies have shown that traditional wireless protocols are highly inefficient or un
Externí odkaz:
http://arxiv.org/abs/2008.13492
Publikováno v:
IEEE Transactions on Network Science and Engineering, 2019
Empirical results show that spatial factors such as distance, population density and communication range affect our social activities, also reflected by the development of ties in social networks. This motivates the need for social network models tha
Externí odkaz:
http://arxiv.org/abs/2003.01489