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

2026-02-17

AI Summary

This work addresses the limitations of existing large language model (LLM)-based approaches in federated cross-domain recommendation, which are prone to overfitting from local low-rank adapters and suffer from difficulties in aligning cross-domain representations. To overcome these challenges, we propose FeDecider, the first framework to effectively leverage LLMs in this setting. FeDecider decouples client-side low-rank updates and aggregates only their directional components to suppress scale-induced noise, while introducing data-aware personalized weights to facilitate effective cross-domain knowledge fusion. Extensive experiments demonstrate that FeDecider significantly outperforms state-of-the-art methods across multiple cross-domain datasets, achieving superior recommendation performance without compromising user privacy.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Data Mining & Knowledge Management: Recommender Systems

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Federated Cross-Domain Recommendation
• Large Language Models
• Overfitting
• Cross-Domain Similarity
• Data Heterogeneity

Innovation

• Federated Cross-Domain Recommendation
• Large Language Models
• Low-Rank Adaptation
• Directional Aggregation
• Personalized Weight Integration