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Found 27 papers, 2 papers with code
Energy-Efficient Edge Learning via Joint Data Deepening-and-Prefetching
The vision of pervasive artificial intelligence (AI) services can be realized by training an AI model on time using real-time data collected by internet of things (IoT) devices.
Knowledge Distillation from Language-Oriented to Emergent Communication for Multi-Agent Remote Control
In this work, we compare emergent communication (EC) built upon multi-agent deep reinforcement learning (MADRL) and language-oriented semantic communication (LSC) empowered by a pre-trained large language model (LLM) using human language.
Generative AI Meets Semantic Communication: Evolution and Revolution of Communication Tasks
Disclosing generative models capabilities in semantic communication paves the way for a paradigm shift with respect to conventional communication systems, which has great potential to reduce the amount of data traffic and offers a revolutionary versatility to novel tasks and applications that were not even conceivable a few years ago.
Mobility-Induced Graph Learning for WiFi Positioning
A smartphone-based user mobility tracking could be effective in finding his/her location, while the unpredictable error therein due to low specification of built-in inertial measurement units (IMUs) rejects its standalone usage but demands the integration to another positioning technique like WiFi positioning.
Towards Semantic Communication Protocols for 6G: From Protocol Learning to Language-Oriented Approaches
neural network-oriented symbolic protocols developed by converting Level 1 MAC outputs into explicit symbols; and Level 3 MAC.
Semantics Alignment via Split Learning for Resilient Multi-User Semantic Communication
In this method, referred to as SL with layer freezing (SLF), each encoder downloads a misaligned decoder, and locally fine-tunes a fraction of these encoder-decoder NN layers.
Language-Oriented Communication with Semantic Coding and Knowledge Distillation for Text-to-Image Generation
By integrating recent advances in large language models (LLMs) and generative models into the emerging semantic communication (SC) paradigm, in this article we put forward to a novel framework of language-oriented semantic communication (LSC).
Sequential Semantic Generative Communication for Progressive Text-to-Image Generation
Our work is expected to pave a new road of utilizing state-of-the-art generative models to real communication systems
SplitAMC: Split Learning for Robust Automatic Modulation Classification
Automatic modulation classification (AMC) is a technology that identifies a modulation scheme without prior signal information and plays a vital role in various applications, including cognitive radio and link adaptation.
Enabling the Wireless Metaverse via Semantic Multiverse Communication
Metaverse over wireless networks is an emerging use case of the sixth generation (6G) wireless systems, posing unprecedented challenges in terms of its multi-modal data transmissions with stringent latency and reliability requirements.
Enabling AI Quality Control via Feature Hierarchical Edge Inference
Specifically, feature network is designed based on feature hierarchy, a one-directional feature dependency with a different scale.
Differentially Private CutMix for Split Learning with Vision Transformer
Split learning (SL) detours this by communicating smashed data at a cut-layer, yet suffers from data privacy leakage and large communication costs caused by high similarity between ViT' s smashed data and input data.
Towards Semantic Communication Protocols: A Probabilistic Logic Perspective
Classical medium access control (MAC) protocols are interpretable, yet their task-agnostic control signaling messages (CMs) are ill-suited for emerging mission-critical applications.
Visual Transformer Meets CutMix for Improved Accuracy, Communication Efficiency, and Data Privacy in Split Learning
Leveraging this, we develop a novel SL framework for ViT, coined CutMixSL, communicating CutSmashed data.
Two-Stage Deep Anomaly Detection with Heterogeneous Time Series Data
We introduce a data-driven anomaly detection framework using a manufacturing dataset collected from a factory assembly line.
Communication-Efficient and Personalized Federated Lottery Ticket Learning
The lottery ticket hypothesis (LTH) claims that a deep neural network (i. e., ground network) contains a number of subnetworks (i. e., winning tickets), each of which exhibiting identically accurate inference capability as that of the ground network.
Federated Knowledge Distillation
The goal of this chapter is to provide a deep understanding of FD while demonstrating its communication efficiency and applicability to a variety of tasks.
Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications
Machine learning (ML) is a promising enabler for the fifth generation (5G) communication systems and beyond.
Mix2FLD: Downlink Federated Learning After Uplink Federated Distillation With Two-Way Mixup
This letter proposes a novel communication-efficient and privacy-preserving distributed machine learning framework, coined Mix2FLD.
XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learning
User-generated data distributions are often imbalanced across devices and labels, hampering the performance of federated learning (FL).
Understanding Uncertainty of Edge Computing: New Principle and Design Approach
Due to the edge's position between the cloud and the users, and the recent surge of deep neural network (DNN) applications, edge computing brings about uncertainties that must be understood separately.
Information Theory Networking and Internet Architecture Information Theory
Proxy Experience Replay: Federated Distillation for Distributed Reinforcement Learning
Traditional distributed deep reinforcement learning (RL) commonly relies on exchanging the experience replay memory (RM) of each agent.
Distilling On-Device Intelligence at the Network Edge
Devices at the edge of wireless networks are the last mile data sources for machine learning (ML).
Multi-hop Federated Private Data Augmentation with Sample Compression
On-device machine learning (ML) has brought about the accessibility to a tremendous amount of data from the users while keeping their local data private instead of storing it in a central entity.
Federated Reinforcement Distillation with Proxy Experience Memory
In distributed reinforcement learning, it is common to exchange the experience memory of each agent and thereby collectively train their local models.
Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data
On-device machine learning (ML) enables the training process to exploit a massive amount of user-generated private data samples.
Blockchained On-Device Federated Learning
By leveraging blockchain, this letter proposes a blockchained federated learning (BlockFL) architecture where local learning model updates are exchanged and verified.
Information Theory Networking and Internet Architecture Information Theory
