Summary of Compressed Bayesian Federated Learning For Reliable Passive Radio Sensing in Industrial Iot, by Luca Barbieri et al.
Compressed Bayesian Federated Learning for Reliable Passive Radio Sensing in Industrial IoT
by Luca Barbieri, Stefano Savazzi, Monica Nicoli
First submitted to arxiv on: 9 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a communication-efficient decentralized Bayesian Federated Learning (FL) policy that reduces communication overhead without sacrificing final learning accuracy and calibration. The approach integrates compression policies, allowing devices to perform multiple optimization steps before sending local posterior distributions. This tool is integrated in an Industrial Internet of Things (IIoT) use case where collaborating nodes equipped with autonomous radar sensors reliably localize human operators in a workplace shared with robots. Numerical results show that the developed approach obtains highly accurate yet well-calibrated ML models compatible with conventional Bayesian FL tools, while decreasing communication overhead up to 99%. The proposed method is also advantageous compared to state-of-the-art compressed frequentist FL setups in terms of calibration, especially when the statistical distribution of the testing dataset changes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a way for machines to learn together without sending too much information. This helps them make good predictions and know how sure they are. The new method makes machines work harder before sharing their answers with each other. This helps reduce the amount of information sent, making it faster and more efficient. It’s tested in a real-life scenario where machines help people working alongside robots. The results show that this new approach is accurate and reliable, reducing communication by up to 99%. It’s also better than existing methods when the data changes. |
Keywords
» Artificial intelligence » Federated learning » Optimization
