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Asynchronous Semi-Decentralized Federated Edge Learning for Heterogeneous Clients
Yuchang Sun, Jiawei Shao, Yuyi Mao +1
Federated edge learning (FEEL) has drawn much attention as a privacy-preserving distributed learning framework for mobile edge networks. In this work, we investigate a novel semi-d…
Semi-Decentralized Federated Edge Learning with Data and Device Heterogeneity
Yuchang Sun, Jiawei Shao, Yuyi Mao +2
Federated edge learning (FEEL) has attracted much attention as a privacy-preserving paradigm to effectively incorporate the distributed data at the network edge for training deep l…
Cell-Free Massive MIMO for 6G Wireless Communication Networks
Hengtao He, Xianghao Yu, Jun Zhang +2
The recently commercialized fifth-generation (5G) wireless communication networks achieved many improvements, including air interface enhancement, spectrum expansion, and network i…
Cell-Free Massive MIMO Detection: A Distributed Expectation Propagation Approach
Hengtao He, Xianghao Yu, Jun Zhang +2
Cell-free massive MIMO is one of the core technologies for future wireless networks. It is expected to bring enormous benefits, including ultra-high reliability, data throughput, e…
How Neural Architectures Affect Deep Learning for Communication Networks?
Yifei Shen, Jun Zhang, Khaled B. Letaief
In recent years, there has been a surge in applying deep learning to various challenging design problems in communication networks. The early attempts adopt neural architectures in…
Learn to Communicate with Neural Calibration: Scalability and Generalization
Yifan Ma, Yifei Shen, Xianghao Yu +3
The conventional design of wireless communication systems typically relies on established mathematical models that capture the characteristics of different communication modules. U…