Federated Fine-Tuning of Sparsely-Activated Large Language Models on Resource-Constrained Devices

Documenti analoghi

• Communication-Efficient Sparsely-Activated Model Training via Sequence Migration and Token Condensation
  di: Chen, Fahao, et al.
  Pubblicazione: (2024)
• A Resource-Adaptive Approach for Federated Learning under Resource-Constrained Environments
  di: Zhang, Ruirui, et al.
  Pubblicazione: (2024)
• SPIN: Accelerating Large Language Model Inference with Heterogeneous Speculative Models
  di: Chen, Fahao, et al.
  Pubblicazione: (2025)
• SFPrompt: Communication-Efficient Split Federated Fine-Tuning for Large Pre-Trained Models over Resource-Limited Devices
  di: Cao, Linxiao, et al.
  Pubblicazione: (2024)
• Efficient Federated Fine-Tuning of Large Language Models with Layer Dropout
  di: Wang, Shilong, et al.
  Pubblicazione: (2025)