Search Results for author:
Found 17 papers, 2 papers with code
Federated In-Context LLM Agent Learning
In this paper, we propose a novel privacy-preserving Federated In-Context LLM Agent Learning (FICAL) algorithm, which to our best knowledge for the first work unleashes the power of in-context learning to train diverse LLM agents through FL.
Generative Semantic Communication: Architectures, Technologies, and Applications
This system features two LLM-based AI agents at both the transmitter and receiver, serving as "brains" to enable powerful information understanding and content regeneration capabilities, respectively.
Receiver-Centric Generative Semantic Communications
To solve this problem, this paper presents a receiver-centric generative semantic communication system, where each transmission is initialized by the receiver.
BANGS: Game-Theoretic Node Selection for Graph Self-Training
Graph self-training is a semi-supervised learning method that iteratively selects a set of unlabeled data to retrain the underlying graph neural network (GNN) model and improve its prediction performance.
Generative Semantic Communication for Text-to-Speech Synthesis
Semantic communication is a promising technology to improve communication efficiency by transmitting only the semantic information of the source data.
DeformStream: Deformation-based Adaptive Volumetric Video Streaming
Volumetric video streaming offers immersive 3D experiences but faces significant challenges due to high bandwidth requirements and latency issues in transmitting detailed content in real time.
Multi-level Personalized Federated Learning on Heterogeneous and Long-Tailed Data
Federated learning (FL) offers a privacy-centric distributed learning framework, enabling model training on individual clients and central aggregation without necessitating data exchange.
Uncertainty in Graph Neural Networks: A Survey
Therefore, identifying, quantifying, and utilizing uncertainty are essential to enhance the performance of the model for the downstream tasks as well as the reliability of the GNN predictions.
Confidence-aware Fine-tuning of Sequential Recommendation Systems via Conformal Prediction
Recognizing the critical role of confidence in aligning training objectives with evaluation metrics, we propose CPFT, a versatile framework that enhances recommendation confidence by integrating Conformal Prediction (CP)-based losses with CE loss during fine-tuning.
NetLLM: Adapting Large Language Models for Networking
Motivated by the recent success of large language models (LLMs), this work studies the LLM adaptation for networking to explore a more sustainable design philosophy.
LMaaS: Exploring Pricing Strategy of Large Model as a Service for Communication
In the first step, we optimize the seller's pricing decision and propose an Iterative Model Pricing (IMP) algorithm that optimizes the prices of large models iteratively by reasoning customers' future rental decisions, which is able to achieve a near-optimal pricing solution.
Data Augmentation for Supervised Graph Outlier Detection via Latent Diffusion Models
To bridge this gap, we introduce GODM, a novel data augmentation for mitigating class imbalance in supervised Graph Outlier detection via latent Diffusion Models.
FedMS: Federated Learning with Mixture of Sparsely Activated Foundations Models
In this paper, we propose a novel two-stage federated learning algorithm called FedMS.
ILCAS: Imitation Learning-Based Configuration-Adaptive Streaming for Live Video Analytics with Cross-Camera Collaboration
The high-accuracy and resource-intensive deep neural networks (DNNs) have been widely adopted by live video analytics (VA), where camera videos are streamed over the network to resource-rich edge/cloud servers for DNN inference.
Learning Cautiously in Federated Learning with Noisy and Heterogeneous Clients
It includes a noise-resilient local solver and a robust global aggregator.
Unimodal Training-Multimodal Prediction: Cross-modal Federated Learning with Hierarchical Aggregation
Multimodal learning has seen great success mining data features from multiple modalities with remarkable model performance improvement.
FSVVD: A Dataset of Full Scene Volumetric Video
Recent years have witnessed a rapid development of immersive multimedia which bridges the gap between the real world and virtual space.
