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Ming Ding — most-cited papers & profile · Federated Learning
← authors
·
overview
Ming Ding
21
papers ·
153
citations ·
32
h-index
Harbin Engineering University
Google Scholar ↗
Semantic Scholar ↗
OpenAlex ↗
Most-cited papers
Federated Learning with Differential Privacy: Algorithms and Performance Analysis
2019 · 88 citations
On Safeguarding Privacy and Security in the Framework of Federated Learning
2019 · 17 citations
Federated Learning for Smart Healthcare: A Survey
2021 · 17 citations
User-Level Privacy-Preserving Federated Learning: Analysis and Performance Optimization
2020 · 12 citations
Blockchain Assisted Decentralized Federated Learning (BLADE-FL) with Lazy Clients
2020 · 5 citations
Low-Latency Federated Learning over Wireless Channels with Differential Privacy
2021 · 5 citations
Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems
2023 · 4 citations
Covert Model Poisoning Against Federated Learning: Algorithm Design and Optimization
2021 · 2 citations
Federated Learning with Unreliable Clients: Performance Analysis and Mechanism Design
2021 · 1 citations
Blockchain Assisted Federated Learning over Wireless Channels: Dynamic Resource Allocation and Client Scheduling
2021 · 1 citations
Federated Meta-Learning for Few-Shot Fault Diagnosis with Representation Encoding
2023 · 1 citations
Analysis and Optimization of Wireless Multimodal Federated Learning on Modal Heterogeneity
2025
Federated Learning with Differential Privacy: An Utility-Enhanced Approach
2025
Multi-Objective Optimization for Privacy-Utility Balance in Differentially Private Federated Learning
2025
Mobility-Aware Joint User Scheduling and Resource Allocation for Low Latency Federated Learning
2023
Top co-authors
Jun Li
· 12
Chuan Ma
· 10
Kang Wei
· 9
H. Vincent Poor
· 7
Wen Chen
· 4
Xiumei Deng
· 4
Aruna Seneviratne
· 3
Dinh C. Nguyen
· 3
Pubudu N. Pathirana
· 3
Xuefeng Han
· 3
Zhu Han
· 3
Howard H. Yang
· 2
Topics
Federated Learning
Privacy
Optimization
Communication
Security
Adversarial Attacks
Edge Computing
Data Heterogeneity