Ming Ding

Blockchain for Secure EHRs Sharing of Mobile Cloud Based E-Health Systems

2019 · IEEE Access

Recent years have witnessed a paradigm shift in the storage of Electronic Health Records (EHRs) on mobile cloud environments, where mobile devices are integrated with cloud computing to facilitate medical data exchanges among patients and …

Client Scheduling in Wireless Federated Learning Based on Channel and Learning Qualities

2022 · IEEE Wireless Communications Letters

Federated learning (FL) emerges as a distributed training method in the Internet of Things (IoT), allowing participating clients to use their local data to train local models and upload parameters for global model aggregation after …

Intelligent Blockchain-based Edge Computing via Deep Reinforcement Learning: Solutions and Challenges

2022 · arXiv (Cornell University)

The convergence of mobile edge computing (MEC) and blockchain is transforming the current computing services in wireless Internet-of-Things networks, by enabling task offloading with security enhancement based on blockchain mining. Yet the existing approaches for …

Rethinking the Setting of Semi-supervised Learning on Graphs

2022 · arXiv (Cornell University)

We argue that the present setting of semisupervised learning on graphs may result in unfair comparisons, due to its potential risk of over-tuning hyper-parameters for models. In this paper, we highlight the significant influence of …

Hierarchical Federated Learning for Social Network with Mobility

2025 · arXiv (Cornell University)

Federated Learning (FL) offers a decentralized solution that allows collaborative local model training and global aggregation, thereby protecting data privacy. In conventional FL frameworks, data privacy is typically preserved under the assumption that local data …

Towards Knowledge-Based Recommender Dialog System

2019

Qibin Chen, Junyang Lin, Yichang Zhang, Ming Ding, Yukuo Cen, Hongxia Yang, Jie Tang. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language …

GCC

2020

Graph representation learning has emerged as a powerful technique for addressing real-world problems. Various downstream graph learning tasks have benefited from its recent developments, such as node classification, similarity search, and graph classification. However, prior …

GLM: General Language Model Pretraining with Autoregressive Blank Infilling

2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Zhengxiao Du, Yujie Qian, Xiao Liu, Ming Ding, Jiezhong Qiu, Zhilin Yang, Jie Tang. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022.