no code implementations • 20 Mar 2024 • Vishnu Pandi Chellapandi, Antesh Upadhyay, Abolfazl Hashemi, Stanislaw H. Żak
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 channels that reflect imperfect information exchange.
no code implementations • 10 Oct 2023 • Liangqi Yuan, Dong-Jun Han, Vishnu Pandi Chellapandi, Stanislaw H. Żak, Christopher G. Brinton
Multimodal federated learning (FL) aims to enrich model training in FL settings where devices are collecting measurements across multiple modalities (e. g., sensors measuring pressure, motion, and other types of data).
no code implementations • 21 Aug 2023 • Vishnu Pandi Chellapandi, Liangqi Yuan, Christopher G. Brinton, Stanislaw H Zak, Ziran Wang
This survey paper presents a review of the advancements made in the application of FL for CAV (FL4CAV).
1 code implementation • 19 Mar 2023 • Vishnu Pandi Chellapandi, Antesh Upadhyay, Abolfazl Hashemi, Stanislaw H /. Zak
The first algorithm, Federated Noisy Decentralized Learning (FedNDL1), comes from the literature, where the noise is added to their parameters to simulate the scenario of the presence of noisy communication channels.
no code implementations • 19 Mar 2023 • Vishnu Pandi Chellapandi, Liangqi Yuan, Stanislaw H /. Zak, Ziran Wang
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 congestion, and pollutant emissions, leading to a safe, efficient, and sustainable transportation system.