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pro vyhledávání: '"Djukic, P"'
Python Testbed for Federated Learning Algorithms (PTB-FLA) is a simple FL framework targeting smart Internet of Things in edge systems that provides both generic centralized and decentralized FL algorithms, which implement the corresponding FL orches
Externí odkaz:
http://arxiv.org/abs/2410.13429
Autor:
Gault, Baptiste, Saksena, Aparna, Sauvage, Xavier, Bagot, Paul, Aota, Leonardo S., Arlt, Jonas, Belkacemi, Lisa T., Boll, Torben, Chen, Yi-Sheng, Daly, Luke, Djukic, Milos B., Douglas, James O., Duarte, Maria J., Felfer, Peter J., Forbes, Richard G., Fu, Jing, Gardner, Hazel M., Gemma, Ryota, Gerstl, Stephan S. A., Gong, Yilun, Hachet, Guillaume, Jakob, Severin, Jenkins, Benjamin M., Jones, Megan E., Khanchandani, Heena, Kontis, Paraskevas, Krämer, Mathias, Kühbach, Markus, Marceau, Ross K. W., Mayweg, David, Moore, Katie L., Nallathambi, Varatharaja, Ott, Benedict C., Poplawsky, Jonathan D, Prosa, Ty, Pundt, Astrid, Saha, Mainak, Schwarz, Tim M., Shang, Yuanyuan, Shen, Xiao, Vrellou, Maria, Yu, Yuan, Zhao, Yujun, Zhao, Huan, Zou, Bowen
As hydrogen is touted as a key player in the decarbonization of modern society, it is critical to enable quantitative H analysis at high spatial resolution, if possible at the atomic scale. Indeed, H has a known deleterious impact on the mechanical p
Externí odkaz:
http://arxiv.org/abs/2405.13158
Recently, Python Testbed for Federated Learning Algorithms emerged as a low code and generative large language models amenable framework for developing decentralized and distributed applications, primarily targeting edge systems, by nonprofessional p
Externí odkaz:
http://arxiv.org/abs/2405.09423
We propose Deep Dict, a deep learning-based lossy time series compressor designed to achieve a high compression ratio while maintaining decompression error within a predefined range. Deep Dict incorporates two essential components: the Bernoulli tran
Externí odkaz:
http://arxiv.org/abs/2401.10396
The Python Testbed for Federated Learning Algorithms is a simple Python FL framework easy to use by ML&AI developers who do not need to be professional programmers, and this paper shows that it is also amenable to emerging AI tools. In this paper, we
Externí odkaz:
http://arxiv.org/abs/2312.04412
Autor:
Djukić, Vladan, Čubrović, Mihailo
Publikováno v:
JHEP04 (2024) 025
We study the holographic interpretation of the bulk instability, i.e. the bulk Lyapunov exponent in the motion of open classical bosonic strings in AdS black hole/brane/string backgrounds. In the vicinity of homogeneous and isotropic horizons the bul
Externí odkaz:
http://arxiv.org/abs/2310.15697
Publikováno v:
Springer, LNCS 14390, 2024
At present many distributed and decentralized frameworks for federated learning algorithms are already available. However, development of such a framework targeting smart Internet of Things in edge systems is still an open challenge. A solution to th
Externí odkaz:
http://arxiv.org/abs/2310.05102
Autor:
Domingo, L., Chehimi, M., Banerjee, S., Yuxun, S. He, Konakanchi, S., Ogunfowora, L., Roy, S., Selvaras, S., Djukic, M., Johnson, C.
The field of drug discovery hinges on the accurate prediction of binding affinity between prospective drug molecules and target proteins, especially when such proteins directly influence disease progression. However, estimating binding affinity deman
Externí odkaz:
http://arxiv.org/abs/2309.03919
Host-Based Network Intrusion Detection via Feature Flattening and Two-stage Collaborative Classifier
Network Intrusion Detection Systems (NIDS) have been extensively investigated by monitoring real network traffic and analyzing suspicious activities. However, there are limitations in detecting specific types of attacks with NIDS, such as Advanced Pe
Externí odkaz:
http://arxiv.org/abs/2306.09451
Nowadays many researchers are developing various distributed and decentralized frameworks for federated learning algorithms. However, development of such a framework targeting smart Internet of Things in edge systems is still an open challenge. In th
Externí odkaz:
http://arxiv.org/abs/2305.20027