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Found 17 papers, 5 papers with code
Neural Representation for Wireless Radiation Field Reconstruction: A 3D Gaussian Splatting Approach
Nevertheless, developing an effective channel modeling approach has been a long-standing challenge.
Dynamics-Aware Gaussian Splatting Streaming Towards Fast On-the-Fly 4D Reconstruction
The recent development of 3D Gaussian Splatting (3DGS) has led to great interest in 4D dynamic spatial reconstruction.
MEGA: Memory-Efficient 4D Gaussian Splatting for Dynamic Scenes
Furthermore, we introduce an entropy-constrained Gaussian deformation technique that uses a deformation field to expand the action range of each Gaussian and integrates an opacity-based entropy loss to limit the number of Gaussians, thus forcing our model to use as few Gaussians as possible to fit a dynamic scene well.
GI-GS: Global Illumination Decomposition on Gaussian Splatting for Inverse Rendering
We present GI-GS, a novel inverse rendering framework that leverages 3D Gaussian Splatting (3DGS) and deferred shading to achieve photo-realistic novel view synthesis and relighting.
EVA-Gaussian: 3D Gaussian-based Real-time Human Novel View Synthesis under Diverse Camera Settings
To address this limitation, we propose a real-time pipeline named EVA-Gaussian for 3D human novel view synthesis across diverse camera settings.
WirelessAgent: Large Language Model Agents for Intelligent Wireless Networks
Wireless networks are increasingly facing challenges due to their expanding scale and complexity.
Bidirectional Stereo Image Compression with Cross-Dimensional Entropy Model
With the rapid advancement of stereo vision technologies, stereo image compression has emerged as a crucial field that continues to draw significant attention.
WirelessLLM: Empowering Large Language Models Towards Wireless Intelligence
To empower LLMs with knowledge and expertise in the wireless domain, this paper proposes WirelessLLM, a comprehensive framework for adapting and enhancing LLMs to address the unique challenges and requirements of wireless communication networks.
Spatial-Aware Latent Initialization for Controllable Image Generation
Recently, text-to-image diffusion models have demonstrated impressive ability to generate high-quality images conditioned on the textual input.
FedCiR: Client-Invariant Representation Learning for Federated Non-IID Features
However, devices often have non-independent and identically distributed (non-IID) data, meaning their local data distributions can vary significantly.
A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency
Without data centralization, FL allows clients to share local information in a privacy-preserving manner.
Large Language Models Empowered Autonomous Edge AI for Connected Intelligence
The evolution of wireless networks gravitates towards connected intelligence, a concept that envisions seamless interconnectivity among humans, objects, and intelligence in a hyper-connected cyber-physical world.
Channel and Gradient-Importance Aware Device Scheduling for Over-the-Air Federated Learning
In this paper, we propose a probabilistic device scheduling framework for over-the-air FL, named PO-FL, to mitigate the negative impact of channel noise, where each device is scheduled according to a certain probability and its model update is reweighted using this probability in aggregation.
Understanding and Improving Model Averaging in Federated Learning on Heterogeneous Data
To gain further insights into model averaging in FL, we decompose the expected loss of the global model into five factors related to the client models.
CFLIT: Coexisting Federated Learning and Information Transfer
We propose a coexisting federated learning and information transfer (CFLIT) communication framework, where the FL and IT devices share the wireless spectrum in an OFDM system.
Reconfigurable Intelligent Surface Empowered Over-the-Air Federated Edge Learning
Federated edge learning (FEEL) has emerged as a revolutionary paradigm to develop AI services at the edge of 6G wireless networks as it supports collaborative model training at a massive number of mobile devices.
Relay-Assisted Cooperative Federated Learning
Then, we analyze the model aggregation error in a single-relay case and show that our relay-assisted scheme achieves a smaller error than the one without relays provided that the relay transmit power and the relay channel gains are sufficiently large.
