64 citations · 123 across the 15 of their papers we have counts for
15 papers
CALVO: Improve Serving Efficiency for LLM Inferences with Intense Network Demands
Weiye Wang, Chen Chen, Junxue Zhang +7
Distributed prefix caching has become a core technique for efficient LLM serving. However, for long-context requests with high cache hit ratios, retrieving reusable KVCache blocks…
Unlocking Full Efficiency of Token Filtering in Large Language Model Training
Di Chai, Pengbo Li, Feiyuan Zhang +7
Token filtering has been proposed to enhance the utility of large language models (LLMs) by eliminating inconsequential tokens during training. While usingfewer tokens is expected…
Swift: Rethinking RDMA Control Plane for Elastic Computing
Junxue Zhang, Han Tian, Xinyang Huang +5
Elastic computing enables dynamic scaling to meet workload demands, and Remote Direct Memory Access (RDMA) enhances this by providing high-throughput, low-latency network communica…
FLASH-FHE: A Heterogeneous Architecture for Fully Homomorphic Encryption Acceleration
Junxue Zhang, Xiaodian Cheng, Gang Cao +6
While many hardware accelerators have recently been proposed to address the inefficiency problem of fully homomorphic encryption (FHE) schemes, none of them is able to deliver opti…
PackVFL: Efficient HE Packing for Vertical Federated Learning
Liu Yang, Shuowei Cai, Di Chai +6
As an essential tool of secure distributed machine learning, vertical federated learning (VFL) based on homomorphic encryption (HE) suffers from severe efficiency problems due to d…
A Survey for Federated Learning Evaluations: Goals and Measures
Di Chai, Leye Wang, Liu Yang +3
Evaluation is a systematic approach to assessing how well a system achieves its intended purpose. Federated learning (FL) is a novel paradigm for privacy-preserving machine learnin…
Linear saturation numbers of Berge- and Berge-
Changxin Wang, Junxue Zhang
The linear saturation number (linear extremal number ) of is the minimum (maximum) number of hyperedges of an…
A Survey on Vertical Federated Learning: From a Layered Perspective
Liu Yang, Di Chai, Junxue Zhang +6
Vertical federated learning (VFL) is a promising category of federated learning for the scenario where data is vertically partitioned and distributed among parties. VFL enriches th…
SoK: Fully Homomorphic Encryption Accelerators
Junxue Zhang, Xiaodian Cheng, Liu Yang +3
Fully Homomorphic Encryption~(FHE) is a key technology enabling privacy-preserving computing. However, the fundamental challenge of FHE is its inefficiency, due primarily to the un…
Secure Forward Aggregation for Vertical Federated Neural Networks
Shuowei Cai, Di Chai, Liu Yang +5
Vertical federated learning (VFL) is attracting much attention because it enables cross-silo data cooperation in a privacy-preserving manner. While most research works in VFL focus…
Practical and Secure Federated Recommendation with Personalized Masks
Liu Yang, Junxue Zhang, Di Chai +4
Federated recommendation addresses the data silo and privacy problems altogether for recommender systems. Current federated recommender systems mainly utilize cryptographic or obfu…
Multi-Objective Congestion Control
Yiqing Ma, Han Tian, Xudong Liao +4
Decades of research on Internet congestion control (CC) has produced a plethora of algorithms that optimize for different performance objectives. Applications face the challenge of…
Practical Lossless Federated Singular Vector Decomposition over Billion-Scale Data
Di Chai, Leye Wang, Junxue Zhang +4
With the enactment of privacy-preserving regulations, e.g., GDPR, federated SVD is proposed to enable SVD-based applications over different data sources without revealing the origi…
FedEval: A Holistic Evaluation Framework for Federated Learning
Di Chai, Leye Wang, Liu Yang +3
Federated Learning (FL) has been widely accepted as the solution for privacy-preserving machine learning without collecting raw data. While new technologies proposed in the past fe…
Quantifying the Performance of Federated Transfer Learning
Qinghe Jing, Weiyan Wang, Junxue Zhang +2
The scarcity of data and isolated data islands encourage different organizations to share data with each other to train machine learning models. However, there are increasing conce…