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Found 5 papers, 1 papers with code
FedNMUT -- Federated Noisy Model Update Tracking Convergence Analysis
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.
FedMFS: Federated Multimodal Fusion Learning with Selective Modality Communication
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).
Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges
This survey paper presents a review of the advancements made in the application of FL for CAV (FL4CAV).
A Survey of Federated Learning for Connected and Automated Vehicles
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.
On the Convergence of Decentralized Federated Learning Under Imperfect Information Sharing
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.
