Fairness and accuracy in horizontal federated learning
In the horizontal federated learning setting, multiple clients jointly train a model under the
coordination of the central server, while the training data is kept on the client to ensure …
coordination of the central server, while the training data is kept on the client to ensure …
Clustered vehicular federated learning: Process and optimization
Federated Learning (FL) is expected to play a prominent role for privacy-preserving machine
learning (ML) in autonomous vehicles. FL involves the collaborative training of a single ML …
learning (ML) in autonomous vehicles. FL involves the collaborative training of a single ML …
Semi-supervised federated learning for travel mode identification from GPS trajectories
GPS trajectories serve as a significant data source for travel mode identification along with
the development of various GPS-enabled smart devices. However, such data directly …
the development of various GPS-enabled smart devices. However, such data directly …
Cross-area travel time uncertainty estimation from trajectory data: a federated learning approach
Along with urbanization and the deployment of GPS sensors in vehicles and mobile phones,
massive amounts of trajectory data have been generated for city areas. The analysis of …
massive amounts of trajectory data have been generated for city areas. The analysis of …
Federated ensemble-learning for transport mode detection in vehicular edge network
Abstract Transport Mode Detection (TMD) has become a crucial part of Intelligent
Transportation Systems (ITS) thanks to the recent advancements in Artificial Intelligence and …
Transportation Systems (ITS) thanks to the recent advancements in Artificial Intelligence and …
Resource-constrained federated edge learning with heterogeneous data: Formulation and analysis
Efficient collaboration between collaborative machine learning and wireless communication
technology, forming a Federated Edge Learning (FEEL), has spawned a series of next …
technology, forming a Federated Edge Learning (FEEL), has spawned a series of next …
Toward crowdsourced transportation mode identification: A semisupervised federated learning approach
Privacy-preserving transportation mode identification (TMI) is among the key challenges
toward future intelligent transportation systems. With recent developments in federated …
toward future intelligent transportation systems. With recent developments in federated …
Lfgurad: A defense against label flip** attack in federated learning for vehicular network
The explosive growth of the interconnected vehicle network creates vast amounts of data
within individual vehicles, offering exciting opportunities to develop advanced applications …
within individual vehicles, offering exciting opportunities to develop advanced applications …
Communication Efficiency and Non-Independent and Identically Distributed Data Challenge in Federated Learning: A Systematic Map** Study
Federated learning has emerged as a promising approach for collaborative model training
across distributed devices. Federated learning faces challenges such as Non-Independent …
across distributed devices. Federated learning faces challenges such as Non-Independent …
Improving transportation mode identification with limited GPS trajectories
The deployment of Global Positioning System (GPS) sensors in modern smartphones and
wearable devices has enabled the acquisition of high-coverage urban trajectories …
wearable devices has enabled the acquisition of high-coverage urban trajectories …