Summary of Lightweight Industrial Cohorted Federated Learning For Heterogeneous Assets, by Madapu Amarlingam et al.
Lightweight Industrial Cohorted Federated Learning for Heterogeneous Assets
by Madapu Amarlingam, Abhishek Wani, Adarsh NL
First submitted to arxiv on: 25 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 In Federated Learning (FL), decentralized Machine Learning (ML) models are trained by exchanging learning between clients without sharing their data. While FL is widely used, it assumes great data similarity or homogeneity, which doesn’t always hold true in industrial settings. When clients have heterogeneous data distributions, FL performance degrades. To address this, we propose a Lightweight Industrial Cohorted FL (LICFL) algorithm that uses model parameters for cohorting without additional on-edge computations and communications. This approach enhances client-level model performance by allowing collaboration with similar clients and training more specialized or personalized models. We also propose an adaptive aggregation algorithm to extend LICFL to Adaptive LICFL (ALICFL), which improves global model performance and speeds up convergence. Our experiments demonstrate the efficacy of these algorithms, comparing their performance to existing approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated Learning is a way for many machines to learn together without sharing their data. This helps keep information private. But, when all the machines have very similar data, it works well. In factories and other industrial settings, this isn’t always the case. The data can be different because of things like machine type or environmental factors. When this happens, Federated Learning doesn’t work as well. To fix this, we created a new way to group machines together based on their data, called Lightweight Industrial Cohorted FL (LICFL). This helps each machine learn better and creates more personalized models. We also came up with an adaptive algorithm that makes it even better. Our tests show that these new algorithms work well and are better than what’s already out there. |
Keywords
* Artificial intelligence * Federated learning * Machine learning
