Summary of Federated Time Series Generation on Feature and Temporally Misaligned Data, by Chenrui Fan et al.
Federated Time Series Generation on Feature and Temporally Misaligned Data
by Chenrui Fan, Zhi Wen Soi, Aditya Shankar, Abele Mălan, Lydia Y. Chen
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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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 FedTDD model is a novel federated learning approach for distributed time series data that addresses the issue of misaligned feature sets and timesteps across clients. Unlike existing models, FedTDD jointly learns a synthesizer to reconcile differences between clients by imputing missing values and features. The model uses a data distillation and aggregation framework to exchange synthetic outputs instead of model parameters, allowing for the transfer of knowledge from one client to another. A coordinator iteratively improves a global distiller network using shared knowledge from clients, which enhances the quality of local feature estimates and allows each client to improve its imputations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FedTDD is a new way to learn with time series data that’s spread across different devices or computers. Right now, it’s hard for these devices to share information because they might have different details and timestamps. The researchers came up with a solution called FedTDD that helps devices work together by filling in the gaps and matching their details. This sharing of information allows each device to get better at predicting what will happen next. The team tested FedTDD on five datasets and found it worked really well, even beating traditional methods. |
Keywords
» Artificial intelligence » Distillation » Federated learning » Time series
