Dense wireless deployments face co-channel interference from heterogeneous sources that vary across base stations (gNBs in 5G). While centralized DNN-based approaches to interference mitigation have shown strong performance, deploying and adapting these models across distributed gNBs via federated learning (FL) requires transmitting full model updates each round, resulting in a cost that scales poorly with network density. Parameter-efficient fine-tuning (PEFT) reduces this burden by training and communicating only a small fraction of parameters. While traditionally applied to large foundation models, we adapt Low-Rank Adaptation (LoRA) to temporal convolutional neural network architectures for interference suppression, placing low-rank adapters on the dilated convolutional layers. This placement enables LoRA to learn local interference-specific temporal patterns, while the frozen backbone retains the shared signal extraction capability. These lightweight adapters (5.1\% of backbone parameters) are federated via FedAvg, reducing per-round communication by up to 20$\times$ compared to federating full model updates. We evaluate various PEFT strategies across simulated distributed gNBs with non-IID interference environments. Results show that local LoRA achieves 12.8\% average BER improvement over the frozen backbone, while Fed-LoRA achieves comparable performance (12.6\%). Fed-LoRA outperforms local adaptation on data-starved nodes where federated knowledge transfer compensates for limited samples, all while avoiding the catastrophic degradation observed with full-model FedAvg under heterogeneous conditions.

翻译：密集无线部署面临来自异构源的共信道干扰，这些干扰源在不同基站（5G中的gNB）间存在差异。尽管基于集中式深度神经网络的干扰抑制方法展现出强大性能，但通过联邦学习在分布式gNB间部署与适配这些模型需每轮传输完整模型更新，导致成本随网络密度增大而急剧上升。参数高效微调通过仅训练和通信少量参数来减轻这一负担。虽然该方法传统上应用于大型基础模型，我们创新性地将低秩适配适配到用于干扰抑制的时域卷积神经网络架构中，在扩张卷积层上部署低秩适配器。这种部署使LoRA能学习局部干扰特有的时域模式，而冻结的主干网络保留共享信号提取能力。这些轻量级适配器（占主干网络参数的5.1%）通过FedAvg进行联邦学习，相比完整模型更新的联邦化，每轮通信量降低达20倍。我们在具有非独立同分布干扰环境的模拟分布式gNB上评估了多种PEFT策略。结果表明，本地LoRA相比冻结主干网络实现平均误码率提升12.8%，而Fed-LoRA达到可比性能（12.6%）。在数据匮乏节点上，Fed-LoRA通过联邦知识迁移补偿有限样本，其性能优于本地适配，同时避免了异构条件下完整模型FedAvg出现的灾难性退化。