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Found 17 papers, 6 papers with code
A Survey on Generative AI and LLM for Video Generation, Understanding, and Streaming
This paper offers an insightful examination of how currently top-trending AI technologies, i. e., generative artificial intelligence (Generative AI) and large language models (LLMs), are reshaping the field of video technology, including video generation, understanding, and streaming.
Towards Message Brokers for Generative AI: Survey, Challenges, and Opportunities
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.
AI-native Interconnect Framework for Integration of Large Language Model Technologies in 6G Systems
The evolution towards 6G architecture promises a transformative shift in communication networks, with artificial intelligence (AI) playing a pivotal role.
Adaptive Compression-Aware Split Learning and Inference for Enhanced Network Efficiency
In this work, we develop an adaptive compression-aware split learning method ('deprune') to improve and train deep learning models so that they are much more network-efficient, which would make them ideal to deploy in weaker devices with the help of edge-cloud resources.
Edge AI Inference in Heterogeneous Constrained Computing: Feasibility and Opportunities
The network edge's role in Artificial Intelligence (AI) inference processing is rapidly expanding, driven by a plethora of applications seeking computational advantages.
A Satellite Imagery Dataset for Long-Term Sustainable Development in United States Cities
Especially satellite imagery is a potential data source for studying sustainable urban development.
Federated Split GANs
Mobile devices and the immense amount and variety of data they generate are key enablers of machine learning (ML)-based applications.
Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration
We claim that to support the constantly growing requirements of intelligent applications in the device-edge-cloud computing continuum, resource orchestration needs to embrace edge AI and emphasize local autonomy and intelligence.
Roadmap for Edge AI: A Dagstuhl Perspective
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.
6G White Paper on Edge Intelligence
We present edge computing along with other 6G enablers as a key component to establish the future 2030 intelligent Internet technologies as shown in this series of 6G White Papers.
Edge Intelligence: Architectures, Challenges, and Applications
Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis in locations close to where data is captured based on artificial intelligence.
Marketplace for AI Models
Artificial intelligence shows promise for solving many practical societal problems in areas such as healthcare and transportation.
Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration: A Survey and Critical Analysis
Periodic re-calibration can improve the accuracy of low-cost sensors, particularly with machine-learning-based calibration, which has shown great promise due to its capability to calibrate sensors in-field.
Privacy-preserving data sharing via probabilistic modelling
Differential privacy allows quantifying privacy loss resulting from accessing sensitive personal data.
IoT-KEEPER: Securing IoT Communications in Edge Networks
The increased popularity of IoT devices have made them lucrative targets for attackers.
Cryptography and Security
Differentially Private Bayesian Learning on Distributed Data
Many applications of machine learning, for example in health care, would benefit from methods that can guarantee privacy of data subjects.
IoT Sentinel: Automated Device-Type Identification for Security Enforcement in IoT
In this paper, we present IOT SENTINEL, a system capable of automatically identifying the types of devices being connected to an IoT network and enabling enforcement of rules for constraining the communications of vulnerable devices so as to minimize damage resulting from their compromise.
Cryptography and Security
