Fangcheng Fu

Orcid: 0000-0003-1658-0380

According to our database1, Fangcheng Fu authored at least 21 papers between 2018 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Retrieval-Augmented Generation for AI-Generated Content: A Survey.
CoRR, 2024

Accelerating Text-to-Image Editing via Cache-Enabled Sparse Diffusion Inference.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
P<sup>2</sup>CG: a privacy preserving collaborative graph neural network training framework.
VLDB J., July, 2023

Angel-PTM: A Scalable and Economical Large-scale Pre-training System in Tencent.
Proc. VLDB Endow., 2023

Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning.
CoRR, 2023

Improving Automatic Parallel Training via Balanced Memory Workload Optimization.
CoRR, 2023

FISEdit: Accelerating Text-to-image Editing via Cache-enabled Sparse Diffusion Inference.
CoRR, 2023

OSDP: Optimal Sharded Data Parallel for Distributed Deep Learning.
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

KVSAgg: Secure Aggregation of Distributed Key-Value Sets.
Proceedings of the 39th IEEE International Conference on Data Engineering, 2023

2022
Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Update.
Proc. VLDB Endow., 2022

Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Updates.
CoRR, 2022

BlindFL: Vertical Federated Machine Learning without Peeking into Your Data.
Proceedings of the SIGMOD '22: International Conference on Management of Data, Philadelphia, PA, USA, June 12, 2022

K-core decomposition on super large graphs with limited resources.
Proceedings of the SAC '22: The 37th ACM/SIGAPP Symposium on Applied Computing, Virtual Event, April 25, 2022

VF-PS: How to Select Important Participants in Vertical Federated Learning, Efficiently and Securely?
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Analyzing Online Transaction Networks with Network Motifs.
Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

2021
VF<sup>2</sup>Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise Learning.
Proceedings of the SIGMOD '21: International Conference on Management of Data, 2021

2020
SKCompress: compressing sparse and nonuniform gradient in distributed machine learning.
VLDB J., 2020

Don't Waste Your Bits! Squeeze Activations and Gradients for Deep Neural Networks via TinyScript.
Proceedings of the 37th International Conference on Machine Learning, 2020

2019
An Experimental Evaluation of Large Scale GBDT Systems.
Proc. VLDB Endow., 2019

2018
SketchML: Accelerating Distributed Machine Learning with Data Sketches.
Proceedings of the 2018 International Conference on Management of Data, 2018

DimBoost: Boosting Gradient Boosting Decision Tree to Higher Dimensions.
Proceedings of the 2018 International Conference on Management of Data, 2018


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