A Comprehensive Federated Learning Scheme Based on the Inference-Efficient Model Design and Buffered Asynchronous Aggregation for Real-Time Traffic Police Gesture Recognition

A Multi-Tier Federated Learning Architecture for Scalable and Efficient Model Training in Autonomous Vehicles

Since autonomous vehicles have been adopted so quickly, there are now more diverse and rich data streaming in real time from vehicle sensors, asking for machine learning models that focus on privacy and efficiency. A new solution called Federated Learning (FL) can learn from large amounts of distributed data without sh...

Deep learning-based multi-class misbehavior classification in fog-enabled vehicular ad hoc networks

This study suggests four classification methods, which include 4 LSTM, 2 LSTM-2 CNN, 3 CNN-1 LSTM, and 2 CNN-1 LSTM within two main classifiers within two main classifiers.

Federated Split Learning-Enabled Adaptive Gradient Quantization Method for the Internet of Vehicles With non-IID Data

A cloud-edge-device collaborative architecture for multimodal LLM-based intelligent apron visual surveillance system

Intelligent apron visual surveillance requires both high inference accuracy and strict real-time performance. However, directly deploying multimodal large language models (MLLMs) in airport apron scenarios introduces significant challenges in latency, energy consumption, and computational resource allocation. To addres...

MAQDRL: QuadTree-Based Multi-Agent Federated Deep Q-Learning for Collision Avoidance and Routing Optimization with MEC-Aware Communication Analysis

Experimental results demonstrate that the proposed reinforcement learning-based Multi-agent Federated Deep Q-Network model, which integrates federated learning with QuadTree spatial partitioning and indexing framework for collision detection, is a promising solution for advancing autonomous vehicle routing systems.

A federated learning and decision transformer-based multi-source data fusion architecture for real-time industrial scheduling dashboards

With the rapid development of industrial IoT and intelligent scheduling systems, real-time fusion of multi-source heterogeneous data and decision optimization have become critical challenges. Traditional centralized data collection methods face privacy leakage and communication bottlenecks, while federated learning (FL...