Fedcomm: A privacy-enhanced and efficient authentication protocol for federated learning in vehicular ad-hoc networks
In vehicular ad-hoc networks (VANET), federated learning enables vehicles to
collaboratively train a global model for intelligent transportation without sharing their local …
collaboratively train a global model for intelligent transportation without sharing their local …
Batch-Aggregate: Efficient Aggregation for Private Federated Learning in VANETs
X Feng, H Liu, H Yang, Q Xie… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Federated learning (FL) in Vehicular Ad-hoc Networks (VANETs) enables vehicles to
collaboratively train machine learning models by aggregating local gradients without …
collaboratively train machine learning models by aggregating local gradients without …
A Privacy-preserving Aggregation Scheme with Continuous Authentication for Federated Learning in VANETs
X Feng, X Wang, H Liu, H Yang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Federated Learning (FL) allows the collaborative training of a global model in Vehicular Ad-
hoc Networks (VANETs): data is maintained on the owner's device and the local gradient …
hoc Networks (VANETs): data is maintained on the owner's device and the local gradient …
A secure and privacy preserved infrastructure for VANETs based on federated learning with local differential privacy
Advancements in Vehicular ad-hoc Network (VANET) technology have led to a growing
network of interconnected devices, including edge devices, resulting in substantial data …
network of interconnected devices, including edge devices, resulting in substantial data …
RSAM: Byzantine-Robust and Secure Model Aggregation in Federated Learning for Internet of Vehicles using Private Approximate Median
Y He, P Li, J Ni, X Deng, H Lu, J Zhang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
In Internet-of-Vehicles (IoVs), Federated Learning (FL) is increasingly used by smart
vehicles to process various sensing data. FL is a collaborative learning approach that …
vehicles to process various sensing data. FL is a collaborative learning approach that …
Feel: Federated end-to-end learning with non-iid data for vehicular ad hoc networks
Recent studies have demonstrated the potentials of federated learning (FL) in achieving
cooperative and privacy-preserving data analytics. It would also be promising if FL can be …
cooperative and privacy-preserving data analytics. It would also be promising if FL can be …
Secure and Efficient Blockchain-Based Federated Learning Approach for VANETs
M Asad, S Shaukat, E Javanmardi… - IEEE Internet of …, 2023 - ieeexplore.ieee.org
The rapid increase in the number of connected vehicles on roads has made Vehicular Ad-
hoc Networks (VANETs) an attractive target for malicious actors. As a result, VANETs require …
hoc Networks (VANETs) an attractive target for malicious actors. As a result, VANETs require …
RCFL: Redundancy-Aware Collaborative Federated Learning in Vehicular Networks
In vehicular networks (VNets), vehicular federated learning (VFL) is a new learning
paradigm that can protect data privacy of vehicle nodes (VNs) while training models. In VFL …
paradigm that can protect data privacy of vehicle nodes (VNs) while training models. In VFL …
Secure federated learning with efficient communication in vehicle network
Y Li, Z Zhang, Z Zhang, YC Kao - Journal of Internet Technology, 2020 - jit.ndhu.edu.tw
Abstract Internet of Vehicles (IoV) generates large amounts of data at the network edge.
Machine learning models are often built on these data, to enable the detection …
Machine learning models are often built on these data, to enable the detection …
A state-of-the-art on federated learning for vehicular communications
M Drissi - Vehicular Communications, 2023 - Elsevier
With the increasing number of connected vehicles on the road, vehicular communications
have become an important research area. Federated learning (FL), a distributed machine …
have become an important research area. Federated learning (FL), a distributed machine …
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