Junkang Liu

According to our database, Junkang Liu authored at least 15 papers between 2024 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

FedBCD:Communication-Efficient Accelerated Block Coordinate Gradient Descent for Federated Learning.

Junkang Liu, Fanhua Shang, Yuanyuan Liu, Hongying Liu, Yuangang Li, YunXiang Gong

CoRR, March, 2026

FedNSAM:Consistency of Local and Global Flatness for Federated Learning.

Junkang Liu, Fanhua Shang, Yuxuan Tian, Hongying Liu, Yuanyuan Liu

CoRR, February, 2026

DP-FedAdamW: An Efficient Optimizer for Differentially Private Federated Large Models.

Jin Liu, Yinbin Miao, Ning Xi, Junkang Liu

CoRR, February, 2026

Rethinking LoRA for Privacy-Preserving Federated Learning in Large Models.

Jin Liu, Yinbin Miao, Ning Xi, Junkang Liu

CoRR, February, 2026

Taming Preconditioner Drift: Unlocking the Potential of Second-Order Optimizers for Federated Learning on Non-IID Data.

Junkang Liu, Fanhua Shang, Hongying Liu, Jin Liu, Weixin An, Yuanyuan Liu

CoRR, February, 2026

FedAdamW: A Communication-Efficient Optimizer with Convergence and Generalization Guarantees for Federated Large Models.

Junkang Liu, Fanhua Shang, Hongying Liu, Yuxuan Tian, Yuanyuan Liu, Jin Liu, Kewen Zhu, Zhouchen Lin

Proceedings of the Fortieth AAAI Conference on Artificial Intelligence, 2026

2025

ILoRA: Federated Learning with Low-Rank Adaptation for Heterogeneous Client Aggregation.

Junchao Zhou, Junkang Liu, Fanhua Shang

CoRR, November, 2025

DP-FedPGN: Finding Global Flat Minima for Differentially Private Federated Learning via Penalizing Gradient Norm.

Junkang Liu, Yuxuan Tian, Fanhua Shang, Yuanyuan Liu, Hongying Liu, Junchao Zhou, Ding Daorui

CoRR, October, 2025

FedAdamW: A Communication-Efficient Optimizer with Convergence and Generalization Guarantees for Federated Large Models.

Junkang Liu, Fanhua Shang, Kewen Zhu, Hongying Liu, Yuanyuan Liu, Jin Liu

CoRR, October, 2025

FedMuon: Accelerating Federated Learning with Matrix Orthogonalization.

Junkang Liu, Fanhua Shang, Junchao Zhou, Hongying Liu, Yuanyuan Liu, Jin Liu

CoRR, October, 2025

FedSWA: Improving Generalization in Federated Learning with Highly Heterogeneous Data via Momentum-Based Stochastic Controlled Weight Averaging.

Junkang Liu, Yuanyuan Liu, Fanhua Shang, Hongying Liu, Jin Liu, Wei Feng

CoRR, July, 2025

Consistency of Local and Global Flatness for Federated Learning.

Junkang Liu, Fanhua Shang, Yuxuan Tian, Hongying Liu, Yuanyuan Liu

Proceedings of the 33rd ACM International Conference on Multimedia, 2025

Improving Generalization in Federated Learning with Highly Heterogeneous Data via Momentum-Based Stochastic Controlled Weight Averaging.

Junkang Liu, Yuanyuan Liu, Fanhua Shang, Hongying Liu, Jin Liu, Wei Feng

Proceedings of the Forty-second International Conference on Machine Learning, 2025

2024

FedBCGD: Communication-Efficient Accelerated Block Coordinate Gradient Descent for Federated Learning.

Junkang Liu, Fanhua Shang, Yuanyuan Liu, Hongying Liu, Yuangang Li, YunXiang Gong

Proceedings of the 32nd ACM International Conference on Multimedia, MM 2024, Melbourne, VIC, Australia, 28 October 2024, 2024

Overview and Prospect of Spiking Neural Networks Training Algorithm.

Zhongchuan Shi, Junkang Liu, Bolong Zheng

Proceedings of the International Conference on Computers, 2024