FedRDAC: Representation–Decision Adaptation and Global–Local Coordination for Personalized Federated LoRA

Federated low-rank adaptation (LoRA) enables parameter-efficient adaptation of pretrained language models without sharing raw client data. However, statistically heterogeneous client data make it difficult to preserve transferable knowledge across clients while supporting client-specific adaptation. Representative personalized federated LoRA methods mainly organize personalization along the representation pathway, leaving decision-level adaptation and the coordination of shared and local components insufficiently unified. We refer to this joint design issue as the personalization coverage–coordination gap. To address it, we propose FedRDAC, a representation–decision adaptation and global–local coordination framework for personalized federated LoRA. Its representation–decision dual adaptation mechanism extends client-specific adaptation across representation and task-decision levels, while its adaptive global–local coordination mechanism controls local contributions and supports module-specific optimization during client training. We evaluate FedRDAC on sentiment classification and semantic equivalence tasks under heterogeneous client partitions using average client accuracy, worst-client accuracy, and cross-client variability. FedRDAC achieves the best average and worst-client results among the evaluated baselines on both tasks. Heterogeneity, hyperparameter sensitivity, and ablation analyses further support the robustness and component-level contributions of the proposed design.