Federated cross-domain recommendation (Federated CDR) aims to collaboratively learn personalized recommendation models across heterogeneous domains while preserving data privacy. Recently, large language model (LLM)-based recommendation models have demonstrated impressive performance by leveraging LLMs' strong reasoning capabilities and broad knowledge. However, adopting LLM-based recommendation models in Federated CDR scenarios introduces new challenges. First, there exists a risk of overfitting with domain-specific local adapters. The magnitudes of locally optimized parameter updates often vary across domains, causing biased aggregation and overfitting toward domain-specific distributions. Second, unlike traditional recommendation models (e.g., collaborative filtering, bipartite graph-based methods) that learn explicit and comparable user/item representations, LLMs encode knowledge implicitly through autoregressive text generation training. This poses additional challenges for effectively measuring the cross-domain similarities under heterogeneity. To address these challenges, we propose an LLM-based framework for federated cross-domain recommendation, FeDecider. Specifically, FeDecider tackles the challenge of scale-specific noise by disentangling each client's low-rank updates and sharing only their directional components. To handle the need for flexible and effective integration, each client further learns personalized weights that achieve the data-aware integration of updates from other domains. Extensive experiments across diverse datasets validate the effectiveness of our proposed FeDecider.

翻译：联邦跨领域推荐（Federated CDR）旨在保护数据隐私的前提下，跨异构领域协同学习个性化推荐模型。近年来，基于大语言模型（LLM）的推荐模型通过利用LLM强大的推理能力和广泛的知识，展现出卓越的性能。然而，在联邦跨领域推荐场景中采用基于LLM的推荐模型带来了新的挑战。首先，存在领域特定本地适配器过拟合的风险。局部优化参数更新的幅度通常在领域间存在差异，导致聚合偏差并过度拟合领域特定分布。其次，与传统推荐模型（例如协同过滤、基于二分图的方法）学习显式且可比较的用户/物品表示不同，LLM通过自回归文本生成训练隐式编码知识。这在异构性下为有效衡量跨领域相似性带来了额外挑战。为解决这些挑战，我们提出了一种基于LLM的联邦跨领域推荐框架FeDecider。具体而言，FeDecider通过解耦每个客户端的低秩更新并仅共享其方向分量，以应对尺度特定噪声的挑战。为满足灵活且有效整合的需求，每个客户端进一步学习个性化权重，以实现对其他领域更新的数据感知整合。跨多个数据集的广泛实验验证了我们提出的FeDecider的有效性。