6 papers
Tessera: Demand-Driven KV Cache Management for Retrieval-Augmented LLM Serving
Fei Fang, Chung-Hsiang Lo, Yi Liu +2
RAG and retrieval-based agent memory both inject retrieved content into LLM prompts, as document chunks and recalled memory records, respectively. The same content can recur across…
Semord: Learned Semantic-Preserving Placement and Low-Fanout Routing for Distributed Vector Search
Shengze Wang, Yi Liu, Yifan Hua +2
Vector databases are increasingly deployed in distributed settings where different users, sites, or domains maintain vector data. Existing vector databases rely on a coordinator to…
Incentivizing High-quality Participation From Federated Learning Agents
Jinlong Pang, Jiaheng Wei, Yifan Hua +2
Federated learning (FL) provides a promising paradigm for facilitating collaboration between multiple clients that jointly learn a global model without directly sharing their local…
PlanetServe: A Decentralized, Scalable, and Privacy-Preserving Overlay for Democratizing Large Language Model Serving
Fei Fang, Yifan Hua, Shengze Wang +4
While significant progress has been made in research and development on open-source and cost-efficient large-language models (LLMs), serving scalability remains a critical challeng…
Towards Practical Overlay Networks for Decentralized Federated Learning
Yifan Hua, Jinlong Pang, Xiaoxue Zhang +5
Decentralized federated learning (DFL) uses peer-to-peer communication to avoid the single point of failure problem in federated learning and has been considered an attractive solu…
Secure Decentralized Learning with Blockchain
Xiaoxue Zhang, Yifan Hua, Chen Qian
Federated Learning (FL) is a well-known paradigm of distributed machine learning on mobile and IoT devices, which preserves data privacy and optimizes communication efficiency. To…