Summary of A Federated Large Language Model For Long-term Time Series Forecasting, by Raed Abdel-sater and A. Ben Hamza
A federated large language model for long-term time series forecasting
by Raed Abdel-Sater, A. Ben Hamza
First submitted to arxiv on: 30 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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 The proposed federated large language model (LLM), called FedTime, is designed for long-range time series prediction in centralized environments. It tackles challenges related to data privacy, communication overhead, and scalability by introducing a pre-trained LLM with fine-tuning and alignment strategies. The model employs K-means clustering to partition edge devices or clients into distinct clusters, patching, and channel independence to preserve local semantic information while minimizing the risk of information loss. FedTime demonstrates substantial improvements over recent approaches on real-world forecasting benchmarks, showcasing its effectiveness and efficiency in streamlining resource usage. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FedTime is a new way to predict what will happen in the future based on past data. It’s special because it can work with lots of devices that have limited power and memory. To do this, it groups these devices into smaller teams and only shares information between them. This helps keep sensitive data private while still getting good results. FedTime is tested on real-world datasets and shows big improvements over other methods. |
Keywords
» Artificial intelligence » Alignment » Clustering » Fine tuning » K means » Large language model » Time series
