Summary of Hstfl: a Heterogeneous Federated Learning Framework For Misaligned Spatiotemporal Forecasting, by Shuowei Cai and Hao Liu
HSTFL: A Heterogeneous Federated Learning Framework for Misaligned Spatiotemporal Forecasting
by Shuowei Cai, Hao Liu
First submitted to arxiv on: 27 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 A novel framework for multi-party collaborative spatiotemporal forecasting is proposed, addressing the challenges of cross-domain feature heterogeneity and geographical heterogeneity. The Heterogeneous SpatioTemporal Federated Learning (HSTFL) framework enables multiple clients to collaboratively harness geo-distributed time series data from different domains while preserving privacy. This approach utilizes vertical federated spatiotemporal representation learning to locally preserve spatiotemporal dependencies among individual participants and generates effective representations for heterogeneous data. A cross-client virtual node alignment block is also proposed, incorporating cross-client spatiotemporal dependencies via a multi-level knowledge fusion scheme. Experimental evaluations demonstrate that HSTFL provides a significant improvement against various baselines while effectively resisting inference attacks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Spatiotemporal forecasting is used in smart city applications like traffic and energy management. This paper talks about how to improve this type of forecasting by combining data from different sources, like real-estate appraisal and human mobility. The problem is that these sources are owned by different parties, so you need a way to share the data without sharing it directly. The authors propose a new framework called HSTFL that allows multiple parties to work together while keeping their data private. This framework uses two main ideas: first, each party does some processing on its own data to make it easier to combine with other data; and second, it matches up patterns in the different data sets so they can be combined effectively. The authors tested this approach and found that it works well and is secure. |
Keywords
» Artificial intelligence » Alignment » Federated learning » Inference » Representation learning » Spatiotemporal » Time series
