Federated Learning and Large Language Models: Recent Advances, Challenges, and Future Directions for Privacy-Preserving AI

Abstract

The rapid advancement of Large Language Models (LLMs) has transformed artificial intelligence by enabling remarkable performance in natural language understanding, generation, and decision-making tasks. However, conventional centralized training approaches require massive amounts of data aggregation, raising significant concerns regarding privacy, security, and regulatory compliance. Federated Learning (FL) has emerged as a promising distributed learning paradigm that enables collaborative model training across decentralized data sources without sharing raw data. The integration of Federated Learning and Large Language Models, commonly referred to as Federated Large Language Models (FedLLMs), offers a novel approach to developing privacy-preserving artificial intelligence systems while maintaining model effectiveness. This review examines recent advances in Federated Large Language Models, focusing on federated pre-training, federated fine-tuning, parameter-efficient adaptation techniques, privacy-preserving mechanisms, and personalized learning strategies. The study analyzes key challenges including communication overhead, data heterogeneity, security vulnerabilities, model bias, scalability limitations, and explainability concerns. Furthermore, the review identifies critical research gaps and proposes an integrated framework that combines privacy protection, secure aggregation, parameter-efficient fine-tuning, personalized learning, and explainable artificial intelligence. Finally, future research directions are discussed to guide the development of scalable, trustworthy, and privacy-aware AI systems. The findings suggest that FedLLMs represent a significant step toward achieving secure and decentralized artificial intelligence across diverse application domains such as healthcare, finance, education, cybersecurity, and Industry 4.0.

Introduction

This text reviews the rise of Large Language Models (LLMs) and the emerging field of Federated Large Language Models (FedLLMs), focusing on how they combine powerful language modeling with privacy-preserving distributed learning.

At its core, it explains that while LLMs like GPT-style models are highly effective due to training on massive centralized datasets, this approach raises serious concerns about privacy, security, ownership, and regulatory compliance—especially in sensitive sectors like healthcare, finance, and government.

To address this, the paper highlights Federated Learning (FL), a decentralized approach where models are trained across multiple devices or organizations without sharing raw data. Instead, only model updates are exchanged. This makes FL well-suited for privacy-sensitive applications.

The combination of FL and LLMs leads to Federated Large Language Models (FedLLMs), which allow collaborative training and fine-tuning while keeping data local. Recent research focuses on improving their practicality through techniques such as federated pre-training, parameter-efficient fine-tuning (like LoRA), secure aggregation, and personalization methods.

However, the review emphasizes major challenges:

• High communication and computational costs due to large model sizes
• Data heterogeneity across clients (non-IID data)
• Security threats like adversarial attacks and model poisoning
• Privacy leakage through shared gradients/updates
• Fairness and bias propagation issues
• Limited explainability and scalability

The paper also surveys applications across healthcare, cybersecurity, industry, and emerging technologies like 6G and quantum systems, showing growing interest in FedLLMs.

Finally, it outlines future research directions such as improving efficiency, strengthening privacy guarantees (e.g., differential privacy), enhancing robustness, enabling personalization, and developing scalable architectures for real-world deployment.

Conclusion

The integration of Federated Learning (FL) and Large Language Models (LLMs) represents a promising approach for developing privacy-preserving, secure, and decentralized artificial intelligence systems. This review examined recent advances, challenges, and future research opportunities in Federated Large Language Models (FedLLMs), highlighting their potential to enable collaborative learning without compromising sensitive data. The findings indicate that FedLLMs can significantly enhance privacy protection while supporting diverse applications across healthcare, finance, education, cybersecurity, Industry 4.0, and intelligent communication networks.

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Copyright

Copyright © 2026 Roopesh Kumar Sharma, Parag Jain, Kirti Sharma, Ujjval Mishra. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.