Search Results for author:
Found 6 papers, 2 papers with code
Adaptive Intrusion Detection in the Networking of Large-Scale LANs with Segmented Federated Learning
We propose Segmented-Federated Learning (Segmented-FL), where by employing periodic local model evaluation and network segmentation, we aim to bring similar network environments to the same group.
Intrusion Detection with Segmented Federated Learning for Large-Scale Multiple LANs
In this research, a segmented federated learning is proposed, different from a collaborative learning based on single global model in a traditional federated learning model, it keeps multiple global models which allow each segment of participants to conduct collaborative learning separately and rearranges the segmentation of participants dynamically as well.
Ranked #1 on
Network Intrusion Detection
on SIDD-Image
Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthiness
Wider coverage and a better solution to a latency reduction in 5G necessitate its combination with multi-access edge computing (MEC) technology.
Reinforcement Learning Based Optimal Camera Placement for Depth Observation of Indoor Scenes
The experimental results indicate that the proposed system outperforms seven out of ten test scenes in obtaining lower depth observation error.
Wireless Ad Hoc Federated Learning: A Fully Distributed Cooperative Machine Learning
WAFL can develop generalized models from Non-IID datasets stored in distributed nodes locally by exchanging and aggregating them with each other over opportunistic node-to-node contacts.
Resilience of Wireless Ad Hoc Federated Learning against Model Poisoning Attacks
Compared to conventional federated learning, WAFL performs model training by weakly synchronizing the model parameters with others, and this shows great resilience to a poisoned model injected by an attacker.
