Zobrazeno 1 - 10
of 98
pro vyhledávání: '"Ozcelebi, Tanir"'
Autor:
Tsouvalas, Vasileios, Mohammadi, Samaneh, Balador, Ali, Ozcelebi, Tanir, Flammini, Francesco, Meratnia, Nirvana
Federated Learning (FL) enables model training across decentralized devices by communicating solely local model updates to an aggregation server. Although such limited data sharing makes FL more secure than centralized approached, FL remains vulnerab
Externí odkaz:
http://arxiv.org/abs/2406.09152
Federated Learning (FL) is a promising technique for the collaborative training of deep neural networks across multiple devices while preserving data privacy. Despite its potential benefits, FL is hindered by excessive communication costs due to repe
Externí odkaz:
http://arxiv.org/abs/2401.14211
Federated Learning (FL) is a distributed machine learning paradigm that enables learning models from decentralized local data. While FL offers appealing properties for clients' data privacy, it imposes high communication burdens for exchanging model
Externí odkaz:
http://arxiv.org/abs/2311.09270
Autor:
van Berlo, Bram, Miao, Yang, Hersyandika, Rizqi, Meratnia, Nirvana, Ozcelebi, Tanir, Kokkeler, Andre, Pollin, Sofie
Joint Communication and Sensing (JCAS) is envisioned for 6G cellular networks, where sensing the operation environment, especially in presence of humans, is as important as the high-speed wireless connectivity. Sensing, and subsequently recognizing b
Externí odkaz:
http://arxiv.org/abs/2210.16951
Federated Learning (FL) is a distributed machine learning paradigm that enables learning models from decentralized private datasets, where the labeling effort is entrusted to the clients. While most existing FL approaches assume high-quality labels a
Externí odkaz:
http://arxiv.org/abs/2208.09378
Speech Emotion Recognition (SER) refers to the recognition of human emotions from natural speech. If done accurately, it can offer a number of benefits in building human-centered context-aware intelligent systems. Existing SER approaches are largely
Externí odkaz:
http://arxiv.org/abs/2202.02611
Federated Learning is a distributed machine learning paradigm dealing with decentralized and personal datasets. Since data reside on devices like smartphones and virtual assistants, labeling is entrusted to the clients, or labels are extracted in an
Externí odkaz:
http://arxiv.org/abs/2107.06877
The increasing bandwidth requirement of new wireless applications has lead to standardization of the millimeter wave spectrum for high-speed wireless communication. The millimeter wave spectrum is part of 5G and covers frequencies between 30 and 300
Externí odkaz:
http://arxiv.org/abs/2012.13664
Smartphones, wearables, and Internet of Things (IoT) devices produce a wealth of data that cannot be accumulated in a centralized repository for learning supervised models due to privacy, bandwidth limitations, and the prohibitive cost of annotations
Externí odkaz:
http://arxiv.org/abs/2007.13018
Deep learning methods are successfully used in applications pertaining to ubiquitous computing, health, and well-being. Specifically, the area of human activity recognition (HAR) is primarily transformed by the convolutional and recurrent neural netw
Externí odkaz:
http://arxiv.org/abs/1907.11879