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cs.IT2026
Exploiting Correlations in Federated Learning: Opportunities and Practical Limitations
Adrian Edin, Michel Kieffer, Mikael Johansson +1
The communication bottleneck in federated learning (FL) has spurred extensive research into techniques to reduce the volume of data exchanged between client devices and the central…
cs.IT2024
Temporal Predictive Coding for Gradient Compression in Distributed Learning
Adrian Edin, Zheng Chen, Michel Kieffer +1
This paper proposes a prediction-based gradient compression method for distributed learning with event-triggered communication. Our goal is to reduce the amount of information tran…
cs.IT2023
Over-the-Air Federated Learning with Compressed Sensing: Is Sparsification Necessary?
Adrian Edin, Zheng Chen
Over-the-Air (OtA) Federated Learning (FL) refers to an FL system where multiple agents apply OtA computation for transmitting model updates to a common edge server. Two important…