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Follow-Me AI: Energy-Efficient User Interaction with Smart Environments
This article introduces Follow-Me AI, a concept designed to enhance user interactions with smart environments, optimize energy use, and provide better control over data captured by these environments.
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.
Device Sampling and Resource Optimization for Federated Learning in Cooperative Edge Networks
Our optimization methodology aims to select the best combination of sampled nodes and data offloading configuration to maximize FedL training accuracy while minimizing data processing and D2D communication resource consumption subject to realistic constraints on the network topology and device capabilities.
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.
On-the-fly Resource-Aware Model Aggregation for Federated Learning in Heterogeneous Edge
Edge computing has revolutionized the world of mobile and wireless networks world thanks to its flexible, secure, and performing characteristics.
Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation
The conventional federated learning (FedL) architecture distributes machine learning (ML) across worker devices by having them train local models that are periodically aggregated by a server.
