11 citations · 13 across the 13 of their papers we have counts for
15 papers
Topology Design for Distributed Consensus with Relay-Assisted Communication
Shuyi Ren, Zheng Chen, Erik G. Larsson
This paper focuses on relay-assisted topology optimization to accelerate the convergence of distributed consensus algorithms over weakly connected networks. Instead of permanently…
Exact Average Consensus under Noisy Communication Links: A Decentralized Gradient Perspective
Yuhang Deng, Zheng Chen, Erik G. Larsson
We study the distributed average consensus problem under persistent link-level disturbances modeled as a martingale difference sequence with uniformly bounded conditional second mo…
Mixed-Timescale Differential Coding for Downlink Model Broadcast in Wireless Federated Learning
Chung-Hsuan Hu, Zheng Chen, Erik G. Larsson
In standard federated learning systems, the parameter server broadcasts the global model to the participating devices in every iteration. Motivated by the temporal correlation betw…
A Unified Framework for Unbiased Non-Coherent Over-the-Air Computation
Martin Dahl, Zheng Chen, Erik G. Larsson
Over-the-Air Computation (OAC) enables efficient data aggregation in large-scale distributed systems by exploiting the superposition property of wireless multiple-access channels.…
Secure Over-the-Air Computation Against Multiple Eavesdroppers using Correlated Artificial Noise
David Nordlund, Luis Maßny, Antonia Wachter-Zeh +2
Over-the-air (OtA) computation enables scalable analog aggregation by exploiting the superposition property of wireless channels, making it an attractive joint communication and co…
A Unified Convergence Analysis for Semi-Decentralized Learning: Sampled-to-Sampled vs. Sampled-to-All Communication
Angelo Rodio, Giovanni Neglia, Zheng Chen +1
In semi-decentralized federated learning, devices primarily rely on device-to-device communication but occasionally interact with a central server. Periodically, a sampled subset o…
Robust and Efficient Average Consensus with Non-Coherent Over-the-Air Aggregation
Yuhang Deng, Zheng Chen, Erik G. Larsson
Non-coherent over-the-air (OTA) computation has garnered increasing attention for its advantages in facilitating information aggregation among distributed agents in resource-constr…
Optimizing Privacy-Utility Trade-off in Decentralized Learning with Generalized Correlated Noise
Angelo Rodio, Zheng Chen, Erik G. Larsson
Decentralized learning enables distributed agents to collaboratively train a shared machine learning model without a central server, through local computation and peer-to-peer comm…
A Unified Activity Detection Framework for Massive Access: Beyond the Block-Fading Paradigm
Jianan Bai, Erik G. Larsson
The wireless channel changes continuously with time and frequency and the block-fading assumption, which is popular in many theoretical analyses, never holds true in practical scen…
Delay-Constrained Grant-Free Random Access in MIMO Systems: Distributed Pilot Allocation and Power Control
Jianan Bai, Zheng Chen, Erik. G. Larsson
We study a delay-constrained grant-free random access system with a multi-antenna base station. The users randomly generate data packets with expiration deadlines, which are then t…
Energy-Efficient Federated Edge Learning with Streaming Data: A Lyapunov Optimization Approach
Chung-Hsuan Hu, Zheng Chen, Erik G. Larsson
Federated learning (FL) has received significant attention in recent years for its advantages in efficient training of machine learning models across distributed clients without di…
Over-the-Air Federated Learning with Phase Noise: Analysis and Countermeasures
Martin Dahl, Erik G. Larsson
Wirelessly connected devices can collaborately train a machine learning model using federated learning, where the aggregation of model updates occurs using over-the-air computation…
Faster Convergence with Less Communication: Broadcast-Based Subgraph Sampling for Decentralized Learning over Wireless Networks
Daniel Pérez Herrera, Zheng Chen, Erik G. Larsson
Consensus-based decentralized stochastic gradient descent (D-SGD) is a widely adopted algorithm for decentralized training of machine learning models across networked agents. A cru…
Detecting Active Attacks in Over-the-Air Computation using Dummy Samples
David Nordlund, Zheng Chen, Erik G. Larsson
Over-the-Air (OtA) computation is a newly emerged concept for computing functions of data from distributed nodes by taking advantage of the wave superposition property of wireless…
Decentralized Learning over Wireless Networks with Broadcast-Based Subgraph Sampling
Daniel Pérez Herrera, Zheng Chen, Erik G. Larsson
This work centers on the communication aspects of decentralized learning over wireless networks, using consensus-based decentralized stochastic gradient descent (D-SGD). Considerin…