Federated Learning for Large Language Models (FedLLM
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Safe-FedLLM: Delving into the Safety of Federated Large Language Models
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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...