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pro vyhledávání: '"Chellapandi, Vishnu Pandi"'
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. This algorithm
Externí odkaz:
http://arxiv.org/abs/2403.13247
Autor:
Yuan, Liangqi, Han, Dong-Jun, Chellapandi, Vishnu Pandi, Żak, Stanislaw H., Brinton, Christopher G.
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). However, key challenges to multi
Externí odkaz:
http://arxiv.org/abs/2310.07048
Autor:
Chellapandi, Vishnu Pandi, Yuan, Liangqi, Brinton, Christopher G., Zak, Stanislaw H, Wang, Ziran
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 for model training presents significant challenges related to in-vehicl
Externí odkaz:
http://arxiv.org/abs/2308.10407
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 vast literature on this topic and most methods centered around the c
Externí odkaz:
http://arxiv.org/abs/2303.10695
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 sustaina
Externí odkaz:
http://arxiv.org/abs/2303.10677
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