From the 1 of 10 linked papers with an AI index.
10 papers
Mixed-Timescale Differential Coding for Downlink Model Broadcast in Wireless Federated Learning
Chung-Hsuan Hu, Zheng Chen, Erik G. Larsson
The paper proposes a mixed‑timescale differential coding scheme that lets devices recover the latest global model in federated learning even when some downlink updates are lost, im…
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 MaÃny, Antonia Wachter-Zeh +2
In the era of the Internet of Things and massive connectivity, many engineering applications, such as sensor fusion and federated edge learning, rely on efficient data aggregation…
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…
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…
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…