Zobrazeno 1 - 10
of 35
pro vyhledávání: '"Murturi, Ilir"'
Advanced wearable sensor devices have enabled the recording of vast amounts of movement data from individuals regarding their physical activities. This data offers valuable insights that enhance our understanding of how physical activities contribute
Externí odkaz:
http://arxiv.org/abs/2408.10191
Autor:
Lackinger, Anna, Frangoudis, Pantelis A., Čilić, Ivan, Furutanpey, Alireza, Murturi, Ilir, Žarko, Ivana Podnar, Dustdar, Schahram
Hierarchical federated learning (HFL) designs introduce intermediate aggregator nodes between clients and the global federated learning server in order to reduce communication costs and distribute server load. One side effect is that machine learning
Externí odkaz:
http://arxiv.org/abs/2407.16836
Federated Learning (FL) has emerged as a promising paradigm to train machine learning models collaboratively while preserving data privacy. However, its widespread adoption faces several challenges, including scalability, heterogeneous data and devic
Externí odkaz:
http://arxiv.org/abs/2311.17958
Autor:
Murturi, Ilir, Donta, Praveen Kumar, Pujol, Victor Casamayor, Morichetta, Andrea, Dustdar, Schahram
Converging Zero Trust (ZT) with learning techniques can solve various operational and security challenges in Distributed Computing Continuum Systems (DCCS). Implementing centralized ZT architecture is seen as unsuitable for the computing continuum (e
Externí odkaz:
http://arxiv.org/abs/2311.17447
Autor:
Guan, Jinglong, Zhang, Qiyang, Murturi, Ilir, Donta, Praveen Kumar, Dustdar, Schahram, Wang, Shangguang
As a driving force in the advancement of intelligent in-orbit applications, DNN models have been gradually integrated into satellites, producing daily latency-constraint and computation-intensive tasks. However, the substantial computation capability
Externí odkaz:
http://arxiv.org/abs/2311.06073
Autor:
Li, Ying, Wang, Xingwei, Zeng, Rongfei, Donta, Praveen Kumar, Murturi, Ilir, Huang, Min, Dustdar, Schahram
Machine learning typically relies on the assumption that training and testing distributions are identical and that data is centrally stored for training and testing. However, in real-world scenarios, distributions may differ significantly and data is
Externí odkaz:
http://arxiv.org/abs/2306.01334
Autor:
Arleo, Alessio, Tsigkanos, Christos, Jia, Chao, Leite, Roger A., Murturi, Ilir, Klaffenboeck, Manfred, Dustdar, Schahram, Wimmer, Michael, Miksch, Silvia, Sorger, Johannes
Investment planning requires knowledge of the financial landscape on a large scale, both in terms of geo-spatial and industry sector distribution. There is plenty of data available, but it is scattered across heterogeneous sources (newspapers, open d
Externí odkaz:
http://arxiv.org/abs/1908.07479
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