Fed-DLoRA: Efficient Wireless Federated Learning with Dynamic Low-Rank Adaptation
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Abstract (arXiv)
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. In this paper, we propose a lightweight FL algorithm named federated learning with dynamic low-rank adaptation (Fed-DLoRA), which is combined with low-rank adaptation (LoRA) to effectively reduce parameters and communication costs while enhancing training efficiency. The convergence analysis of Fed-DLoRA is conducted through stochastic gradient descent optimization coupled with singular value decomposition. This analysis establishes the theoretical relationships among LoRA rank, vehicular scheduling strategies and the model's convergence characteristics. Building on these insights, we formulate a joint optimization problem aimed at maximizing system performance. To address this problem, we propose an adaptive rank, bandwidth and vehicle selection (ARBVS) algorithm that integrates enumeration with greedy optimization strategies. The algorithm provides efficient rank selection and resource scheduling strategies for each FL communication round, thereby achieving effective performance improvements for the FL system. Experimental results demonstrate that Fed-DLoRA achieves superior performance compared to conventional federated learning approaches, exhibiting enhanced accuracy, faster convergence, and improved communication efficiency.
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References · 16
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
- 2019Model Pruning Enables Efficient Federated Learning on Edge Devicescited by 222
- 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
- 2020Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challengescited by 139
- 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
- 2024Flora: Low-Rank Adapters Are Secretly Gradient Compressorscited by 108
- 2020Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learningcited by 90
- 2024Selective Aggregation for Low-Rank Adaptation in Federated Learningcited by 80
- 2023Differentially Private Low-Rank Adaptation of Large Language Model Using Federated Learningcited by 41
- 2021Mobility-Aware Cluster Federated Learning in Hierarchical Wireless Networkscited by 35
- 2023FedRA: A Random Allocation Strategy for Federated Tuning to Unleash the Power of Heterogeneous Clientscited by 19
4 more
- 2022Resource Constrained Vehicular Edge Federated Learning with Highly Mobile Connected Vehiclescited by 15
- 2021Efficient and Private Federated Learning with Partially Trainable Networkscited by 13
- 2024ConvLoRA and AdaBN based Domain Adaptation via Self-Trainingcited by 10
- 2024DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devicescited by 9
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