Federated Personal Knowledge Graph Completion with Lightweight Large Language Models for Personalized Recommendations
arXiv:2603.13264v1 Announce Type: new Abstract: Personalized recommendation increasingly relies on private user data, motivating approaches that can adapt to individuals without centralizing their information. We present Federated Targeted Recommendations with Evolving Knowledge graphs and Language Models (FedTREK-LM), a framework that unifies lightweight large language models (LLMs), evolving personal knowledge graphs (PKGs), federated learning (FL), and Kahneman-Tversky Optimization to enable scalable, decentralized personalization. By prompting LLMs with structured PKGs, FedTREK-LM performs context-aware reasoning for personalized recommendation tasks such as movie and recipe suggestions. Across three lightweight Qwen3 models (0.6B, 1.7B, 4B), FedTREK-LM consistently and substantially outperforms state-of-the-art KG completion and federated recommendation baselines (HAKE, KBGAT, and FedKGRec), achieving more than a 4x improvement in F1-score on the movie and food benchmarks. Our re
arXiv:2603.13264v1 Announce Type: new Abstract: Personalized recommendation increasingly relies on private user data, motivating approaches that can adapt to individuals without centralizing their information. We present Federated Targeted Recommendations with Evolving Knowledge graphs and Language Models (FedTREK-LM), a framework that unifies lightweight large language models (LLMs), evolving personal knowledge graphs (PKGs), federated learning (FL), and Kahneman-Tversky Optimization to enable scalable, decentralized personalization. By prompting LLMs with structured PKGs, FedTREK-LM performs context-aware reasoning for personalized recommendation tasks such as movie and recipe suggestions. Across three lightweight Qwen3 models (0.6B, 1.7B, 4B), FedTREK-LM consistently and substantially outperforms state-of-the-art KG completion and federated recommendation baselines (HAKE, KBGAT, and FedKGRec), achieving more than a 4x improvement in F1-score on the movie and food benchmarks. Our results further show that real user data is critical for effective personalization, as synthetic data degrades performance by up to 46%. Overall, FedTREK-LM offers a practical paradigm for adaptive, LLM-powered personalization that generalizes across decentralized, evolving user PKGs.
Executive Summary
This study introduces Federated Targeted Recommendations with Evolving Knowledge Graphs and Language Models (FedTREK-LM), a framework that combines lightweight large language models, evolving personal knowledge graphs, federated learning, and Kahneman-Tversky Optimization to facilitate decentralized personalization. FedTREK-LM utilizes context-aware reasoning to provide personalized recommendations, achieving significant improvements over state-of-the-art KG completion and federated recommendation baselines. The framework's practicality is demonstrated through its ability to generalize across decentralized and evolving user knowledge graphs, with real user data being essential for effective personalization. However, synthetic data is found to degrade performance by up to 46%, highlighting the importance of authentic user input.
Key Points
- ▸ FedTREK-LM integrates multiple technologies to provide scalable and decentralized personalization
- ▸ The framework achieves substantial improvements over existing KG completion and federated recommendation methods
- ▸ Real user data is crucial for effective personalization, as synthetic data significantly degrades performance
Merits
Strength in Personalization
FedTREK-LM's use of lightweight large language models and evolving personal knowledge graphs enables context-aware reasoning, resulting in more accurate and personalized recommendations.
Scalability and Decentralization
The framework's integration of federated learning and Kahneman-Tversky Optimization allows for scalable and decentralized personalization, making it suitable for large-scale applications.
Generalizability
FedTREK-LM demonstrates the ability to generalize across decentralized and evolving user knowledge graphs, making it a versatile solution for various recommendation tasks.
Demerits
Synthetic Data Limitations
The study highlights the significant degradation of performance when using synthetic data, emphasizing the importance of authentic user input for effective personalization.
Resource-Intensive Training
The framework's reliance on lightweight large language models and evolving personal knowledge graphs may require substantial computational resources and training time, potentially limiting its adoption in resource-constrained environments.
Expert Commentary
While FedTREK-LM demonstrates significant advancements in decentralized personalization, its reliance on lightweight large language models and evolving personal knowledge graphs raises concerns about computational resources and training time. Furthermore, the study's emphasis on real user data underscores the need for more robust data protection and governance frameworks. As AI-powered personalization continues to gain traction, it is essential to address these concerns and explore ways to make FedTREK-LM and similar frameworks more accessible and sustainable.
Recommendations
- ✓ Future research should focus on developing more efficient and resource-friendly versions of FedTREK-LM, leveraging emerging technologies such as edge AI and distributed computing.
- ✓ Developers and policymakers should prioritize the development of robust data protection and governance frameworks to ensure the secure and responsible use of user data in AI-powered personalization systems.