Federated Large Language Models: Current Progress and Future Directions

Understand

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).