Rethinking Resource Management in Edge Learning: A Joint Pre-training and Fine-tuning Design Paradigm

Documenti analoghi

• AI-in-the-Loop Sensing and Communication Joint Design for Edge Intelligence
  di: Cai, Zhijie, et al.
  Pubblicazione: (2025)
• FeedSign: Robust Full-parameter Federated Fine-tuning of Large Models with Extremely Low Communication Overhead of One Bit
  di: Cai, Zhijie, et al.
  Pubblicazione: (2025)
• Closing the Generalization Gap in Parameter-efficient Federated Edge Learning
  di: Du, Xinnong, et al.
  Pubblicazione: (2025)
• FlexSpec: Frozen Drafts Meet Evolving Targets in Edge-Cloud Collaborative LLM Speculative Decoding
  di: Li, Yuchen, et al.
  Pubblicazione: (2026)
• Three Birds, One Stone: Solving the Communication-Memory-Privacy Trilemma in LLM Fine-tuning Over Wireless Networks with Zeroth-Order Optimization
  di: Cai, Zhijie, et al.
  Pubblicazione: (2026)