Summary of Applied Federated Model Personalisation in the Industrial Domain: a Comparative Study, by Ilias Siniosoglou et al.
Applied Federated Model Personalisation in the Industrial Domain: A Comparative Study
by Ilias Siniosoglou, Vasileios Argyriou, George Fragulis, Panagiotis Fouliras, Georgios Th. Papadopoulos, Anastasios Lytos, Panagiotis Sarigiannidis
First submitted to arxiv on: 10 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Performance (cs.PF)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach is proposed to tackle the challenges of training and deploying complex Machine Learning (ML) and Deep Learning (DL) models in federated settings, where individual nodes’ optimization proves difficult. The study suggests three strategies: Active Learning, Knowledge Distillation, and Local Memorization, which enable smaller model adoption with reduced computational resources and personalized insights for improved accuracy. An advanced Federated Learning System is designed to utilize these personalization methods in real-time NG-IoT applications, investigating efficacy in local and federated domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models are getting smarter, but it takes a lot of computing power to train them. This makes it hard to use these models on devices that don’t have as much power. The researchers found three ways to make this better: using less data to learn, copying knowledge from one model to another, and making smaller models that work well locally. They also created a new system that combines these ideas to make machine learning more efficient and personalized for different users. |
Keywords
» Artificial intelligence » Active learning » Deep learning » Federated learning » Knowledge distillation » Machine learning » Optimization
