Search Results for author:
Found 11 papers, 4 papers with code
PackVFL: Efficient HE Packing for Vertical Federated Learning
To this core, we propose PackVFL, an efficient VFL framework based on packed HE (PackedHE), to accelerate the existing HE-based VFL algorithms.
A Survey for Federated Learning Evaluations: Goals and Measures
Evaluation is a systematic approach to assessing how well a system achieves its intended purpose.
UCTB: An Urban Computing Tool Box for Building Spatiotemporal Prediction Services
To address these issues, we design and implement a spatiotemporal crowd flow prediction toolbox called UCTB (Urban Computing Tool Box), which integrates multiple spatiotemporal domain knowledge and state-of-the-art models simultaneously.
A Survey on Vertical Federated Learning: From a Layered Perspective
From the hardware layer to the vertical federated system layer, researchers contribute to various aspects of VFL.
Secure Forward Aggregation for Vertical Federated Neural Networks
In this paper, we focus on SplitNN, a well-known neural network framework in VFL, and identify a trade-off between data security and model performance in SplitNN.
Practical and Secure Federated Recommendation with Personalized Masks
In this paper, we proposed federated masked matrix factorization (FedMMF) to protect the data privacy in federated recommender systems without sacrificing efficiency and effectiveness.
Aegis: A Trusted, Automatic and Accurate Verification Framework for Vertical Federated Learning
In this paper, we present Aegis, a trusted, automatic, and accurate verification framework to verify the security of VFL jobs.
FedEval: A Holistic Evaluation Framework for Federated Learning
In this paper, we propose a holistic evaluation framework for FL called FedEval, and present a benchmarking study on seven state-of-the-art FL algorithms.
Exploring the Generalizability of Spatio-Temporal Traffic Prediction: Meta-Modeling and an Analytic Framework
The Spatio-Temporal Traffic Prediction (STTP) problem is a classical problem with plenty of prior research efforts that benefit from traditional statistical learning and recent deep learning approaches.
Secure Federated Matrix Factorization
The key principle of federated learning is training a machine learning model without needing to know each user's personal raw private data.
Bike Flow Prediction with Multi-Graph Convolutional Networks
We propose a new multi-graph convolutional neural network model to predict the bike flow at station-level, where the key novelty is viewing the bike sharing system from the graph perspective.
