Fed-DLoRA combines low-rank adaptation with federated learning and an adaptive rank-bandwidth-vehicle selection algorithm to improve accuracy, convergence speed, and communication efficiency in wireless IoV environments.
Joint class- balanced client selection and bandwidth allocation for cost-efficient federated learning in mobile edge computing networks
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Fed-DLoRA: Efficient Wireless Federated Learning with Dynamic Low-Rank Adaptation
Fed-DLoRA combines low-rank adaptation with federated learning and an adaptive rank-bandwidth-vehicle selection algorithm to improve accuracy, convergence speed, and communication efficiency in wireless IoV environments.