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Found 9 papers, 3 papers with code
Effective Intrusion Detection in Highly Imbalanced IoT Networks with Lightweight S2CGAN-IDS
The proposed framework leverages the distribution characteristics of network traffic to expand the number of minority categories in both data space and feature space, resulting in a substantial increase in the detection rate of minority categories while simultaneously ensuring the detection precision of majority categories.
Enabling AI-Generated Content (AIGC) Services in Wireless Edge Networks
To achieve efficient AaaS and maximize the quality of generated content in wireless edge networks, we propose a deep reinforcement learning-enabled algorithm for optimal ASP selection.
HFedMS: Heterogeneous Federated Learning with Memorable Data Semantics in Industrial Metaverse
Federated Learning (FL), as a rapidly evolving privacy-preserving collaborative machine learning paradigm, is a promising approach to enable edge intelligence in the emerging Industrial Metaverse.
Personalized Saliency in Task-Oriented Semantic Communications: Image Transmission and Performance Analysis
Semantic communication, as a promising technology, has emerged to break through the Shannon limit, which is envisioned as the key enabler and fundamental paradigm for future 6G networks and applications, e. g., smart healthcare.
Encoded Gradients Aggregation against Gradient Leakage in Federated Learning
However, privacy information could be leaked from uploaded gradients and be exposed to malicious attackers or an honest-but-curious server.
Cross-Silo Heterogeneous Model Federated Multitask Learning
CoFED is a federated learning method that is compatible with heterogeneous models, tasks, and training processes.
Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoT
In this paper, we propose FedGS, which is a hierarchical cloud-edge-end FL framework for 5G empowered industries, to improve industrial FL performance on non-i. i. d.
Heterogeneous Federated Learning via Grouped Sequential-to-Parallel Training
In this paper, we propose a data heterogeneity-robust FL approach, FedGSP, to address this challenge by leveraging on a novel concept of dynamic Sequential-to-Parallel (STP) collaborative training.
Reinforcement Learning based QoS/QoE-aware Service Function Chaining in Software-Driven 5G Slices
With the ever growing diversity of devices and applications that will be connected to 5G networks, flexible and agile service orchestration with acknowledged QoE that satisfies end-user's functional and QoS requirements is necessary.
