FeDecider: An LLM-Based Framework for Federated Cross-Domain Recommendation

• URL: http://arxiv.org/abs/2602.16034v1
• Date: Tue, 17 Feb 2026 21:42:28 GMT
• Title: FeDecider: An LLM-Based Framework for Federated Cross-Domain Recommendation
• Authors: Xinrui He, Ting-Wei Li, Tianxin Wei, Xuying Ning, Xinyu He, Wenxuan Bao, Hanghang Tong, Jingrui He,
• Abstract summary: Large language model (LLM)-based recommendation models have demonstrated impressive performance.<n>We propose an LLM-based framework for Federated cross-domain recommendation, FeDecider.<n>Extensive experiments across diverse datasets validate the effectiveness of our proposed FeDecider.
• Score: 75.50721642765994
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Replacing Parameters with Preferences: Federated Alignment of Heterogeneous Vision-Language Models [63.70401095689976]
  We argue that replacing parameters with preferences represents a more scalable and privacy-preserving future.<n>We propose MoR, a federated alignment framework based on GRPO with Mixture-of-Rewards for heterogeneous VLMs.<n>MoR consistently outperforms federated alignment baselines in generalization, robustness, and cross-client adaptability.
  arXiv  Detail & Related papers  (2026-01-31T03:11:51Z)
• LLM-EDT: Large Language Model Enhanced Cross-domain Sequential Recommendation with Dual-phase Training [53.539682966282534]
  Cross-domain Sequential Recommendation (CDSR) has been proposed to enrich user-item interactions by incorporating information from various domains.<n>Despite current progress, the imbalance issue and transition issue hinder further development of CDSR.<n>We propose an LLMs Enhanced Cross-domain Sequential Recommendation with Dual-phase Training (LLM-EDT)
  arXiv  Detail & Related papers  (2025-11-25T05:18:04Z)
• WeaveRec: An LLM-Based Cross-Domain Sequential Recommendation Framework with Model Merging [24.949880939628386]
  We introduce WeaveRec, which cross-trains multiple LoRA modules with source and target domain data in a weaving fashion.<n>We provide a theoretical guarantee that WeaveRec can reduce the upper bound of the expected error in the target domain.
  arXiv  Detail & Related papers  (2025-10-30T14:37:15Z)
• Heterogeneous User Modeling for LLM-based Recommendation [70.52873882470328]
  A key challenge to advancing open-domain recommendation is effectively modeling user preferences from users' heterogeneous behaviors.<n>Existing approaches, including ID-based and semantic-based modeling, struggle with poor generalization.<n>We propose a Heterogeneous User Modeling (HUM) method, which incorporates a compression enhancer and a robustness enhancer for LLM-based recommendation.
  arXiv  Detail & Related papers  (2025-07-07T03:08:28Z)
• LLM-Enhanced Multimodal Fusion for Cross-Domain Sequential Recommendation [32.40055370439922]
  Cross-Domain Sequential Recommendation (CDSR) predicts user behavior by leveraging historical interactions across multiple domains.<n>We propose LLM-Enhanced Multimodal Fusion for Cross-Domain Sequential Recommendation (LLM-EMF)<n>LLM-EMF is a novel and advanced approach that enhances textual information with Large Language Models (LLM) knowledge.
  arXiv  Detail & Related papers  (2025-06-22T09:53:21Z)
• FlowerTune: A Cross-Domain Benchmark for Federated Fine-Tuning of Large Language Models [43.62847972139202]
  Large Language Models (LLMs) have achieved state-of-the-art results across diverse domains, yet their development remains reliant on vast amounts of publicly available data.<n>This work lays the foundation for developing privacy-preserving, domain-specialized LLMs for real-world applications.
  arXiv  Detail & Related papers  (2025-06-03T14:54:12Z)
• Personalized Federated Fine-Tuning for LLMs via Data-Driven Heterogeneous Model Architectures [23.45003063073793]
  Federated learning (FL) enables collaborative fine-tuning of large language models without accessing raw data.<n>Existing approaches to federated fine-tuning usually adopt a uniform model architecture.<n>We propose FedAMoLE, a lightweight personalized FL framework that enables data-driven heterogeneous model architectures.
  arXiv  Detail & Related papers  (2024-11-28T13:20:38Z)
• FedCCRL: Federated Domain Generalization with Cross-Client Representation Learning [4.703379311088474]
  Domain Generalization (DG) aims to train models that can effectively generalize to unseen domains. In Federated Learning (FL), where clients collaboratively train a model without directly sharing their data, most existing DG algorithms are not directly applicable to the FL setting. We propose FedCCRL, a lightweight federated domain generalization method that significantly improves the model's generalization ability while preserving privacy.
  arXiv  Detail & Related papers  (2024-10-15T04:44:21Z)
• Exploring User Retrieval Integration towards Large Language Models for Cross-Domain Sequential Recommendation [66.72195610471624]
  Cross-Domain Sequential Recommendation aims to mine and transfer users' sequential preferences across different domains. We propose a novel framework named URLLM, which aims to improve the CDSR performance by exploring the User Retrieval approach.
  arXiv  Detail & Related papers  (2024-06-05T09:19:54Z)
• FACT: Federated Adversarial Cross Training [0.0]
  Federated Adrial Cross Training (FACT) uses implicit domain differences between source clients to identify domain shifts in the target domain. We empirically show that FACT outperforms state-of-the-art federated, non-federated and source-free domain adaptation models.
  arXiv  Detail & Related papers  (2023-06-01T12:25:43Z)
• Diverse Preference Augmentation with Multiple Domains for Cold-start Recommendations [92.47380209981348]
  We propose a Diverse Preference Augmentation framework with multiple source domains based on meta-learning. We generate diverse ratings in a new domain of interest to handle overfitting on the case of sparse interactions. These ratings are introduced into the meta-training procedure to learn a preference meta-learner, which produces good generalization ability.
  arXiv  Detail & Related papers  (2022-04-01T10:10:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.