Publications (5)
Towards Secure and Private AI: A Framework for Decentralized Inference
Hongyang Zhang, Yue Zhao, Claudio Angione +5
The rapid advancement of ML models in critical sectors such as healthcare, finance, and security has intensified the need for robust data security, model integrity, and reliable ou…
Encrypted Large Model Inference: The Equivariant Encryption Paradigm
James Buban, Hongyang Zhang, Claudio Angione +10
Large scale deep learning model, such as modern language models and diffusion architectures, have revolutionized applications ranging from natural language processing to computer v…
Model Agnostic Hybrid Sharding For Heterogeneous Distributed Inference
Claudio Angione, Yue Zhao, Harry Yang +4
The rapid growth of large-scale AI models, particularly large language models has brought significant challenges in data privacy, computational resources, and accessibility. Tradit…
Meta-Learning for Speeding Up Large Model Inference in Decentralized Environments
Yuzhe Yang, Yipeng Du, Ahmad Farhan +6
The deployment of large-scale models, such as large language models (LLMs) and sophisticated image generation systems, incurs substantial costs due to their computational demands.…
Meta-Learning for Speeding Up Large Model Inference in Decentralized Environments
Yipeng Du, Zihao Wang, Ahmad Farhan +7
The deployment of large-scale models, such as large language models (LLMs), incurs substantial costs due to their computational demands. To mitigate these costs and address challen…