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Found 6 papers, 1 papers with code
Enhancing Image Privacy in Semantic Communication over Wiretap Channels leveraging Differential Privacy
Semantic communication (SemCom) enhances transmission efficiency by sending only task-relevant information compared to traditional methods.
FeDeRA:Efficient Fine-tuning of Language Models in Federated Learning Leveraging Weight Decomposition
FeDeRA follows LoRA by decomposing the weight matrices of the PLMs into low-rank matrices, which allows for more efficient computation and parameter updates during fine-tuning.
Secure Semantic Communication for Image Transmission in the Presence of Eavesdroppers
To address this issue, we propose a novel secure semantic communication (SemCom) approach for image transmission, which integrates steganography technology to conceal private information within non-private images (host images).
Evolving Semantic Communication with Generative Model
In this paper, we explore an evolving semantic communication system for image transmission, referred to as ESemCom, with the capability to continuously enhance transmission efficiency.
Computational Intelligence and Deep Learning for Next-Generation Edge-Enabled Industrial IoT
In this paper, we investigate how to deploy computational intelligence and deep learning (DL) in edge-enabled industrial IoT networks.
Battery-constrained Federated Edge Learning in UAV-enabled IoT for B5G/6G Networks
In this paper, we study how to optimize the federated edge learning (FEEL) in UAV-enabled Internet of things (IoT) for B5G/6G networks, from a deep reinforcement learning (DRL) approach.
Federated Learning
Networking and Internet Architecture
