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Mingzhe Chen — most-cited papers & profile · Federated Learning
← authors
·
overview
Mingzhe Chen
31
papers ·
358
citations ·
54
h-index
University of Miami
Google Scholar ↗
Semantic Scholar ↗
OpenAlex ↗
Most-cited papers
A Joint Learning and Communications Framework for Federated Learning over Wireless Networks
2019 · 108 citations
Energy Efficient Federated Learning Over Wireless Communication Networks
2019 · 53 citations
Delay Minimization for Federated Learning Over Wireless Communication Networks
2020 · 53 citations
Convergence Time Optimization for Federated Learning over Wireless Networks
2020 · 33 citations
Distributed Learning in Wireless Networks: Recent Progress and Future Challenges
2021 · 28 citations
Federated Learning for Task and Resource Allocation in Wireless High Altitude Balloon Networks
2020 · 19 citations
Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarms
2020 · 15 citations
Federated Learning on the Road: Autonomous Controller Design for Connected and Autonomous Vehicles
2021 · 11 citations
Wireless Communications for Collaborative Federated Learning
2020 · 9 citations
Digital Over-the-Air Federated Learning in Multi-Antenna Systems
2023 · 3 citations
Optimization of User Selection and Bandwidth Allocation for Federated Learning in VLC/RF Systems
2021 · 2 citations
On Differential Privacy for Federated Learning in Wireless Systems with Multiple Base Stations
2022 · 2 citations
Optimizing Communication and Device Clustering for Clustered Federated Learning with Differential Privacy
2025
Efficient Split Federated Learning for Large Language Models over Communication Networks
2025
FedVQCS: Federated Learning via Vector Quantized Compressed Sensing
2022
Top co-authors
Walid Saad
· 10
H. Vincent Poor
· 7
Zhaohui Yang
· 6
and Shuguang Cui
· 4
Changchuan Yin
· 4
Choong Seon Hong
· 3
Shuguang Cui
· 3
Sihua Wang
· 3
and Mehdi Bennis
· 2
Christopher G. Brinton
· 2
Mohammad Shikh‐Bahaei
· 2
Omid Semiari
· 2
Topics
Federated Learning
Optimization
Communication
Privacy
Edge Computing
Data Heterogeneity