Summary of Advancing Iiot with Over-the-air Federated Learning: the Role Of Iterative Magnitude Pruning, by Fazal Muhammad Ali Khan et al.
Advancing IIoT with Over-the-Air Federated Learning: The Role of Iterative Magnitude Pruning
by Fazal Muhammad Ali Khan, Hatem Abou-Zeid, Aryan Kaushik, Syed Ali Hassan
First submitted to arxiv on: 21 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 The proposed integration of federated learning (FL) with industrial Internet of Things (IIoT) enables devices to learn locally without sharing confidential data. Edge sensors, or peripheral intelligence units (PIUs), can adapt using their own data, facilitating a collaborative yet private learning process. To make deep neural network (DNN) models suitable for PIUs’ limited resources, model compression techniques like pruning and iterative magnitude pruning (IMP) are applied to reduce the size of DNNs while maintaining performance. This research explores IMP’s effectiveness in an over-the-air FL (OTA-FL) environment for IIoT. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning makes it possible for devices in Industry 4.0 to work together and learn from each other without sharing their private data. This is important because devices need to be able to make decisions based on the data they collect, but they don’t always have access to the internet or a central server. To make this happen, researchers are working on ways to compress deep neural networks so that they can fit on these devices and work efficiently. One approach is called iterative magnitude pruning (IMP), which helps reduce the size of the network while keeping its performance strong. |
Keywords
* Artificial intelligence * Federated learning * Model compression * Neural network * Pruning
