Zobrazeno 1 - 10
of 63
pro vyhledávání: '"Pirttikangas, Susanna"'
This paper presents a fully coupled blockchain-assisted federated learning architecture that effectively eliminates single points of failure by decentralizing both the training and aggregation tasks across all participants. Our proposed system offers
Externí odkaz:
http://arxiv.org/abs/2406.00181
In today's digital world, Generative Artificial Intelligence (GenAI) such as Large Language Models (LLMs) is becoming increasingly prevalent, extending its reach across diverse applications. This surge in adoption has sparked a significant increase i
Externí odkaz:
http://arxiv.org/abs/2312.14647
Autor:
Zhang, Shouhua, Zhou, Jiehan, Ma, Xue, Wen, Chenglin, Pirttikangas, Susanna, Yu, Chen, Zhang, Weishan, Yang, Chunsheng
Traditional fault diagnosis methods using Convolutional Neural Networks (CNNs) face limitations in capturing temporal features (i.e., the variation of vibration signals over time). To address this issue, this paper introduces a novel model, the Time
Externí odkaz:
http://arxiv.org/abs/2311.06916
To support the stringent requirements of the future intelligent and interactive applications, intelligence needs to become an essential part of the resource management in the edge environment. Developing intelligent orchestration solutions is a chall
Externí odkaz:
http://arxiv.org/abs/2311.01224
Autor:
Lovén, Lauri, Morabito, Roberto, Kumar, Abhishek, Pirttikangas, Susanna, Riekki, Jukka, Tarkoma, Sasu
This paper proposes the neural publish/subscribe paradigm, a novel approach to orchestrating AI workflows in large-scale distributed AI systems in the computing continuum. Traditional centralized broker methodologies are increasingly struggling with
Externí odkaz:
http://arxiv.org/abs/2309.02058
Autor:
Gilman, Ekaterina, Keskinarkaus, Anja, Tamminen, Satu, Pirttikangas, Susanna, Röning, Juha, Riekki, Jukka
Recent advances in technology are changing the way how everyday activities are performed. Technologies in the traffic domain provide diverse instruments of gathering and analysing data for more fuel-efficient, safe, and convenient travelling for both
Externí odkaz:
https://publish.fid-move.qucosa.de/id/qucosa%3A72830
https://publish.fid-move.qucosa.de/api/qucosa%3A72830/attachment/ATT-0/
https://publish.fid-move.qucosa.de/api/qucosa%3A72830/attachment/ATT-0/
Autor:
Kokkonen, Henna, Lovén, Lauri, Motlagh, Naser Hossein, Kumar, Abhishek, Partala, Juha, Nguyen, Tri, Pujol, Víctor Casamayor, Kostakos, Panos, Leppänen, Teemu, González-Gil, Alfonso, Sola, Ester, Angulo, Iñigo, Liyanage, Madhusanka, Bennis, Mehdi, Tarkoma, Sasu, Dustdar, Schahram, Pirttikangas, Susanna, Riekki, Jukka
Future AI applications require performance, reliability and privacy that the existing, cloud-dependant system architectures cannot provide. In this article, we study orchestration in the device-edge-cloud continuum, and focus on edge AI for resource
Externí odkaz:
http://arxiv.org/abs/2205.01423
Efficient Vehicle-to-Everything enabling cooperation and enhanced decision-making for autonomous vehicles is essential for optimized and safe traffic. Real-time decision-making based on vehicle sensor data, other traffic data, and environmental and c
Externí odkaz:
http://arxiv.org/abs/2204.03313
Smartwatches have rapidly evolved towards capabilities to accurately capture physiological signals. As an appealing application, stress detection attracts many studies due to its potential benefits to human health. It is propitious to investigate the
Externí odkaz:
http://arxiv.org/abs/2108.12502
Autor:
Zhou, Jiehan, Zhang, Shouhua, Lu, Qinghua, Dai, Wenbin, Chen, Min, Liu, Xin, Pirttikangas, Susanna, Shi, Yang, Zhang, Weishan, Herrera-Viedma, Enrique
Federated learning (FL) brings collaborative intelligence into industries without centralized training data to accelerate the process of Industry 4.0 on the edge computing level. FL solves the dilemma in which enterprises wish to make the use of data
Externí odkaz:
http://arxiv.org/abs/2104.10501