11 citations · 13 across the 10 of their papers we have counts for
11 papers
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.…
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…
Distributed Average Consensus in Wireless Multi-Agent Systems with Over-the-Air Aggregation
Themistoklis Charalambous, Zheng Chen, Christoforos N. Hadjicostis
In this paper, we address the average consensus problem of multi-agent systems over wireless networks. We propose a distributed average consensus algorithm by invoking the concept…
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…
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…
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…