11 citations · 19 across the 8 of their papers we have counts for
8 papers
Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data
Abhijit Chunduru, Majid Morafah, Mahdi Morafah +2
The inevitable presence of data heterogeneity has made federated learning very challenging. There are numerous methods to deal with this issue, such as local regularization, better…
FedNMUT -- Federated Noisy Model Update Tracking Convergence Analysis
Vishnu Pandi Chellapandi, Antesh Upadhyay, Abolfazl Hashemi +1
A novel Decentralized Noisy Model Update Tracking Federated Learning algorithm (FedNMUT) is proposed that is tailored to function efficiently in the presence of noisy communication…
A Review of Full-Sized Autonomous Racing Vehicle Sensor Architecture
Manuel Mar, Vishnu Chellapandi, Liangqi Yuan +2
In the landscape of technological innovation, autonomous racing is a dynamic and challenging domain that not only pushes the limits of technology, but also plays a crucial role in…
FedMFS: Federated Multimodal Fusion Learning with Selective Modality Communication
Liangqi Yuan, Dong-Jun Han, Vishnu Pandi Chellapandi +2
Multimodal federated learning (FL) aims to enrich model training in FL settings where devices are collecting measurements across multiple modalities (e.g., sensors measuring pressu…
Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges
Vishnu Pandi Chellapandi, Liangqi Yuan, Christopher G. Brinton +2
Machine learning (ML) is widely used for key tasks in Connected and Automated Vehicles (CAV), including perception, planning, and control. However, its reliance on vehicular data f…
On the Convergence of Decentralized Federated Learning Under Imperfect Information Sharing
Vishnu Pandi Chellapandi, Antesh Upadhyay, Abolfazl Hashemi +1
Decentralized learning and optimization is a central problem in control that encompasses several existing and emerging applications, such as federated learning. While there exists…
A Survey of Federated Learning for Connected and Automated Vehicles
Vishnu Pandi Chellapandi, Liangqi Yuan, Stanislaw H /. Zak +1
Connected and Automated Vehicles (CAVs) are one of the emerging technologies in the automotive domain that has the potential to alleviate the issues of accidents, traffic congestio…
Towards End-to-End Deep Learning for Autonomous Racing: On Data Collection and a Unified Architecture for Steering and Throttle Prediction
Shakti N. Wadekar, Benjamin J. Schwartz, Shyam S. Kannan +5
Deep Neural Networks (DNNs) which are trained end-to-end have been successfully applied to solve complex problems that we have not been able to solve in past decades. Autonomous dr…