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.
(A.2) Under Assumption 3 (bounded variance), the first term in (A.2) is bounded byσ 2P α2 n, leading to: C3 ≤ η2β 2 σ2 |S|X n=1 α2 n +E |S|X n=1 αn∇Ln(xn(t)) 2
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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.