Unextractable Protocol Models: Collaborative Training and Inference without Weight Materialization
Researchers have introduced Unextractable Protocol Models (UPMs), a new framework for collaborative training and inference of neural networks where individual participants only process subsets of the model. This approach ensures that a complete set of model weights is never available to any single entity by periodically injecting time-varying transforms. UPMs demonstrate minimal impact on perplexity and add only a small overhead in latency, bandwidth, and memory during inference and training. AI
IMPACT Enables secure collaborative AI development by preventing model extraction, potentially facilitating community-driven training initiatives.