Summary of Adversarial Federated Consensus Learning For Surface Defect Classification Under Data Heterogeneity in Iiot, by Jixuan Cui et al.
Adversarial Federated Consensus Learning for Surface Defect Classification Under Data Heterogeneity in IIoT
by Jixuan Cui, Jun Li, Zhen Mei, Yiyang Ni, Wen Chen, Zengxiang Li
First submitted to arxiv on: 24 Sep 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 paper proposes a novel federated learning approach called Adversarial Federated Consensus Learning (AFedCL) for industrial surface defect classification (SDC). The challenge is data scarcity and heterogeneity, which hinders the application of deep learning in SDC due to privacy concerns. AFedCL addresses this issue by developing a dynamic consensus construction strategy, an adversarial training mechanism, a consensus-aware aggregation mechanism, and an adaptive feature fusion module. These components aim to mitigate performance degradation caused by data heterogeneity, alleviate global knowledge forgetting, enhance the global model’s generalization capabilities, and optimize global and local features. Experimental results show that AFedCL achieves an accuracy increase of up to 5.67% compared to state-of-the-art federated learning methods like FedALA on three SDC datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a way to use deep learning for industrial surface defect classification without sharing the data. It’s hard to get enough training data because it’s private, and also the data might be different from one place to another. The new approach, called AFedCL, helps by making sure all the local models are working together well, so they don’t forget what they’ve learned. It also makes sure that each model is using its own strengths and weaknesses in the right way. This helps the final result become better. In fact, it did get 5.67% better than other methods on three different datasets. |
Keywords
» Artificial intelligence » Classification » Deep learning » Federated learning » Generalization
