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This survey consolidates current progress in Federated Learning for Large Language Models, addressing critical hurdles like data heterogeneity and communication overhead while highlighting emerging directions such as federated pre-training and agents. By focusing on practical solutions for efficiency, personalization, and security, the paper offers a structured roadmap for researchers navigating the transition from centralized to decentralized model training. The work is particularly relevant for teams evaluating the feasibility of deploying privacy-preserving LLMs across distributed client environments.
Federated Large Language Models: Current Progress and Future Directions
arXiv:2409.15723v3 Announce Type: replace Abstract: Large Language Models have achieved impressive performance across diverse applications, yet their training typically depends on centralized data collection, raising serious privacy and governance concerns. Federated Learning offers a decentralized alternative by enabling multiple clients to collaboratively train shared models without exposing raw local data. However, integrating FL with LLMs introduces new challenges, including data heterogeneity, convergence instability, communication overhead, and computational constraints. This survey provides a comprehensive and up-to-date overview of Federated Learning for Large Language Models (FedLLM). We systematically review recent advances, with particular emphasis on federated fine-tuning and federated prompt learning, and analyze how existing methods address efficiency, personalization, and security challenges. We further summarize emerging directions such as federated pre-training and federated agents. Our goal is to offer a structured perspective on this rapidly evolving field and to highlight promising avenues for future research.
Abstract:Large Language Models have achieved impressive performance across diverse applications, yet their training typically depends on centralized data collection, raising serious privacy and governance concerns. Federated Learning offers a decentralized alternative by enabling multiple clients to collaboratively train shared models without exposing raw local data. However, integrating FL with LLMs introduces new challenges, including data heterogeneity, convergence instability, communication overhead, and computational constraints. This survey provides a comprehensive and up-to-date overview of Federated Learning for Large Language Models (FedLLM). We systematically review recent advances, with particular emphasis on federated fine-tuning and federated prompt learning, and analyze how existing methods address efficiency, personalization, and security challenges. We further summarize emerging directions such as federated pre-training and federated agents. Our goal is to offer a structured perspective on this rapidly evolving field and to highlight promising avenues for future research.
Machine Learning (cs.LG); Computation and Language (cs.CL)
arXiv:2409.15723 [cs.LG]
(or arXiv:2409.15723v3 [cs.LG] for this version)
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