Daniel Ramage

Daniel Ramage is a researcher at Google focused on privacy-preserving machine learning and federated learning. His work bridges artificial intelligence and data privacy, with particular emphasis on decentralized data systems, secure model training, and privacy-aware NLP. He has published extensively on topics including local differential privacy, attack resilience in federated systems, and collaborative model development for mobile applications.

• Key Research Areas:
  • Federated Learning Architectures
  • Privacy-Preserving AI
  • Language Model Security
  • Collaborative Machine Learning
• Recent Publication Trends:
  • 2025: Trustworthy inference mechanisms
  • 2024: Error correction in mobile LLMs
  • 2023: Production-scale federated systems