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

Encoder-Sharing Hierarchical Federated Multi-Task Learning for VANETs

This paper proposes encoder-sharing hierarchical multi-task federated learning (EN-HMTFL), which integrates cluster-based hierarchical federated learning with a globally shared encoder and vehicle-local decoders.

Resilient Decentralized Wireless Federated Learning via Gradient Tracking with AdamW

Experimental results on heterogeneous MNIST and CIFAR-10 settings show that QEF-GT-AdamW consistently improves robustness and convergence performance over representative DecL baselines while achieving favorable accuracy-communication trade-offs under limited wireless resources.

EA-AQF: energy aware adaptive quantization and freezing in federated learning

Energy-Aware Adaptive Quantization and Freezing (EA-AQF), a unified framework that co-optimizes communication and computation, is presented, a unified framework that co-optimizes communication and computation and maintains robust convergence in highly heterogeneous tasks.

Mobility-Aware Multi-Model Fully Decentralized Federated Learning Framework for Vehicular Edge Computing

Decentralized federated learning (DFL) has been introduced in vehicular networks to enable collaborative model training among clients through model exchange instead of sharing sensitive data, thereby preserving privacy and eliminating reliance on centralized infrastructure. Meanwhile, with the rapid growth of vehicular...

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

Maximizing Accuracy-Per-Cost in Vehicular Federated Learning via Active Inference and Two-Stage Resource Optimization

Federated Learning (FL) enables collaborative model training in the Internet of Vehicles (IoV), helping vehicles overcome data and computational limitations while preserving privacy. However, its performance is constrained by both data heterogeneity and system heterogeneity. Additionally, different FL tasks exhibit div...