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FedViT: Federated continual learning of vision transformer at edge

Zuo, Xiaojiang (author), Luopan, Yaxin (author), Han, Rui (author), Zhang, Qinglong (author), Liu, Chi Harold (author), Wang, Guoren (author), Chen, Lydia Y. (author)

Deep Neural Networks (DNNs) have been ubiquitously adopted in internet of things and are becoming an integral part of our daily life. When tackling the evolving learning tasks in real world, such as classifying different types of objects, DNNs face the challenge to continually retrain themselves according to the tasks on different edge...

journal article 2024

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Adaptive approximate computing in edge AI and IoT applications: A review

Damsgaard, Hans Jakob (author), Grenier, Antoine (author), Katare, D. (author), Taufique, Zain (author), Shakibhamedan, Salar (author), Troccoli, Tiago (author), Chatzitsompanis, Georgios (author), Kanduri, Anil (author), Ding, Aaron Yi (author)

Recent advancements in hardware and software systems have been driven by the deployment of emerging smart health and mobility applications. These developments have modernized the traditional approaches by replacing conventional computing systems with cyber–physical and intelligent systems combining the Internet of Things (IoT) with Edge...

review 2024

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Reliable and Energy-efficient Diabetic Retinopathy Screening using Memristor-based Neural Networks

Diware, S.S. (author), Chilakala, Koteswararao (author), Joshi, Rajiv V. (author), Hamdioui, S. (author), Bishnoi, R.K. (author)

Diabetic retinopathy (DR) is a leading cause of permanent vision loss worldwide. It refers to irreversible retinal damage caused due to elevated glucose levels and blood pressure. Regular screening for DR can facilitate its early detection and timely treatment. Neural network-based DR classifiers can be leveraged to achieve such screening in...

journal article 2024

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Cooperative Task Execution for Object Detection in Edge Computing: An Internet of Things Application

Amanatidis, Petros (author), Karampatzakis, Dimitris (author), Iosifidis, G. (author), Lagkas, Thomas (author), Nikitas, Alexandros (author)

The development of computer hardware and communications has brought with it many exciting applications in the Internet of Things. More and more Single Board Computers (SBC) with high performance and low power consumption are used to infer deep learning models at the edge of the network. In this article, we investigate a cooperative task...

journal article 2023

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Reinforcement Learning for Intelligent Healthcare Systems: A Review of Challenges, Applications, and Open Research Issues

Abdellatif, Alaa Awad (author), Mhaisen, N. (author), Mohamed, Amr (author), Erbad, Aiman (author), Guizani, Mohsen (author)

The rise of chronic disease patients and the pandemic pose immediate threats to healthcare expenditure and mortality rates. This calls for transforming healthcare systems away from one-on-one patient treatment into intelligent health systems, leveraging the recent advances of Internet of Things and smart sensors. Meanwhile, reinforcement...

journal article 2023

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A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services

Katare, D. (author), Perino, Diego (author), Nurmi, Jari (author), Warnier, Martijn (author), Janssen, M.F.W.H.A. (author), Ding, Aaron Yi (author)

Autonomous driving services depends on active sensing from modules such as camera, LiDAR, radar, and communication units. Traditionally, these modules process the sensed data on high-performance computing units inside the vehicle, which can deploy intelligent algorithms and AI models. The sensors mentioned above can produce large volumes of data...

journal article 2023

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Improved DQN-Based Computation Offloading Algorithm in MEC Environment

Zhao, Zheyu (author), Cheng, H. (author), Xu, Xiaohua (author)

Massive terminal users have brought explosive need of data residing at edge of overall network. Multiple Mobile Edge Computing (MEC) servers are built in/near base station to meet this need. However, optimal distribution of these servers to multiple users in real time is still a problem. Reinforcement Learning (RL) as a framework to solve...

conference paper 2023

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EdgeVisionBench: A Benchmark of Evolving Input Domains for Vision Applications at Edge

Zhang, Qinglong (author), Han, Rui (author), Liu, Chi Harold (author), Wang, Guoren (author), Chen, Lydia Y. (author)

Vision applications powered by deep neural networks (DNNs) are widely deployed on edge devices and solve the learning tasks of incoming data streams whose class label and input feature continuously evolve, known as domain shift. Despite its prominent presence in real-world edge scenarios, existing benchmarks used by domain adaptation methods...

conference paper 2023

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EdgeBOL: A Bayesian Learning Approach for the Joint Orchestration of vRANs and Mobile Edge AI

Ayala-Romero, Jose A. (author), Garcia-Saavedra, Andres (author), Costa-Perez, Xavier (author), Iosifidis, G. (author)

Future mobile networks need to support intelligent services which collect and process data streams at the network edge, so as to offer real-time and accurate inferences to users. However, the widespread deployment of these services is hindered by the unprecedented energy cost they induce to the network, and by the difficulties in optimizing...

journal article 2023

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Federated Learning for Online Resource Allocation in Mobile Edge Computing: A Deep Reinforcement Learning Approach

Zheng, Jingjing (author), Li, Kai (author), Mhaisen, N. (author), Ni, Wei (author), Tovar, Eduardo (author), Guizani, Mohsen (author)

Federated learning (FL) is increasingly considered to circumvent the disclosure of private data in mobile edge computing (MEC) systems. Training with large data can enhance FL learning accuracy, which is associated with non-negligible energy use. Scheduled edge devices with small data save energy but decrease FL learning accuracy due to a...

conference paper 2023

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B4W: A Smart Wireless Intruder Detection System

Sharma, Suryansh (author), Venkata T., Prabhakar (author), Singhal, Shalakha (author), Kuma, Gogineni Gopi Sunanth (author), Venkatesha Prasad, Ranga Rao (author)

Surveillance and monitoring are highly critical in many application scenarios like wildlife conservation, restricted areas such as nuclear spillover, and border security. Moreover, in these scenarios, intrusions do not happen frequently thus, conventional surveillance is overkill and expensive that also requires extensive human involvement which...

conference paper 2023

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Graph Partition and Multiple Choice-UCB Based Algorithms for Edge Server Placement in MEC Environment

Zhao, Zheyu (author), Cheng, H. (author), Xu, Xiaohua (author), Pan, Yi (author)

The deployment of edge servers make a significant impact on the service quality of a Mobile Edge Computing (MEC) system. This service quality relies on solving two key sub-problems: 1) interference management between servers 2) the placement of MEC servers. To improve the Quality of Service (QoS), we propose a method based on Graph Partition ...

journal article 2023

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Migration-aware Network Services with Edge Computing

Mukhopadhyay, Atri (author), Iosifidis, G. (author), Ruffini, Marco (author)

The development of Multi-access edge computing (MEC) has resulted from the requirement for supporting next generation mobile services, which need high capacity, high reliability and low latency. The key issue in such MEC architectures is to decide which edge nodes will be employed for serving the needs of the different end users. Here, we...

journal article 2022

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User-Defined Privacy-Preserving Traffic Monitoring Against n-by-1 Jamming Attack

Li, Meng (author), Zhu, Liehuang (author), Zhang, Zijian (author), Lal, C. (author), Conti, M. (author), Alazab, Mamoun (author)

Traffic monitoring services collect traffic reports and respond to users' traffic queries. However, the reports and queries may reveal the user's identity and location. Although different anonymization techniques have been applied to protect user privacy, a new security threat arises, namely, n-by-1 jamming attack, in which an anonymous...

journal article 2022

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On Designing Smart Agents for Service Provisioning in Blockchain-Powered Systems

Mhaisen, N. (author), Allahham, Mhd Saria (author), Mohamed, Amr (author), Erbad, Aiman (author), Guizani, Mohsen (author)

Service provisioning systems assign users to service providers according to allocation criteria that strike an optimal trade-off between users' Quality of Experience (QoE) and the operation cost endured by providers. These systems have been leveraging Smart Contracts (SCs) to add trust and transparency to their criteria. However, deploying...

journal article 2022

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Selective Edge Computing for Mobile Analytics

Galanopoulos, Apostolos (author), Iosifidis, G. (author), Salonidis, Theodoros (author), Leith, Douglas J. (author)

An increasing number of mobile applications rely on Machine Learning (ML) routines for analyzing data. Executing such tasks at the user devices saves the energy spent on transmitting and processing large data volumes at distant cloud-deployed servers. However, due to memory and computing limitations, the devices often cannot support the...

journal article 2022

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Roadmap for edge AI: A Dagstuhl Perspective

Ding, Aaron Yi (author), Peltonen, Ella (author), Meuser, Tobias (author), Aral, Atakan (author), Becker, Christian (author), Dustdar, Schahram (author), Hiessl, Thomas (author), Kranzlmüller, Dieter (author), Liyanage, Madhusanka (author), Maghsudi, Setareh (author), Mohan, Nitinder (author), Ott, Jörg (author), Rellermeyer, Jan S. (author), Schulte, Stefan (author), Schulzrinne, Henning (author), Solmaz, Gürkan (author), Tarkoma, Sasu (author), Varghese, Blesson (author), Wolf, Lars (author)

Based on the collective input of Dagstuhl Seminar (21342), this paper presents a comprehensive discussion on AI methods and capabilities in the context of edge computing, referred as Edge AI. In a nutshell, we envision Edge AI to provide adaptation for data-driven applications, enhance network and radio access, and allow the creation,...

journal article 2022

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Federated Learning With Heterogeneity-Aware Probabilistic Synchronous Parallel on Edge

Zhao, Jianxin (author), Han, Rui (author), Yang, Yongkai (author), Catterall, Benjamin (author), Liu, Chi Harold (author), Chen, Lydia Y. (author), Mortier, Richard (author), Crowcroft, Jon (author), Wang, Liang (author)

With the massive amount of data generated from mobile devices and the increase of computing power of edge devices, the paradigm of Federated Learning has attracted great momentum. In federated learning, distributed and heterogeneous nodes collaborate to learn model parameters. However, while providing benefits such as privacy by design and...

journal article 2022

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Exploring Deep Reinforcement Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Preserving EdgeIoT

Zheng, Jingjing (author), Li, Kai (author), Mhaisen, N. (author), Ni, Wei (author), Tovar, Eduardo (author), Guizani, Mohsen (author)

Federated learning (FL) has been increasingly considered to preserve data training privacy from eavesdropping attacks in mobile-edge computing-based Internet of Things (EdgeIoT). On the one hand, the learning accuracy of FL can be improved by selecting the IoT devices with large data sets for training, which gives rise to a higher energy...

journal article 2022

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Demo Abstract: Catch My Eye: Gaze-Based Activity Recognition in an Augmented Reality Art Gallery

Scargill, Tim (author), Lan, G. (author), Gorlatova, Maria (author)

The personalization of augmented reality (AR) experiences based on environmental and user context is key to unlocking their full potential. The recent addition of eye tracking to AR headsets provides a convenient method for detecting user context, but complex analysis of raw gaze data is required to detect where a user's attention and thoughts...

conference paper 2022