AEGIS: Scaling Long-Sequence Homomorphic Encrypted Transformer Inference via Hybrid Parallelism on Multi-GPU Systems

1. 1Implement Fully Homomorphic Encryption (FHE) for privacy-preserving Transformer inference
2. 2Use hybrid parallelism to scale long-sequence encrypted Transformers on multi-GPU systems
3. 3Optimize communication between GPUs to reduce overhead induced by application-level aggregation and encryption-level operations

AI engineers and researchers working on privacy-preserving machine learning models can benefit from AEGIS to improve the scalability of their models, while data scientists can apply this technique to protect sensitive data

💡 Hybrid parallelism can be used to scale long-sequence homomorphic encrypted Transformers on multi-GPU systems, improving privacy-preserving machine learning

🔒 AEGIS scales long-sequence homomorphic encrypted Transformer inference on multi-GPU systems!