AI RESEARCH
Fed-DLoRA: Efficient Wireless Federated Learning with Dynamic Low-Rank Adaptation
arXiv CS.LG
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ArXi:2604.24103v1 Announce Type: new Federated learning (FL) offers a promising distributed learning paradigm for internet of vehicles (IoV) applications. However, it faces challenges from communication overhead and dynamic environments. Model compression techniques reduce computing and communication burden yet create trade-offs between compression ratios and vehicle participation strategies.