Summary of Individualized Federated Learning For Traffic Prediction with Error Driven Aggregation, by Hang Chen et al.
Individualized Federated Learning for Traffic Prediction with Error Driven Aggregation
by Hang Chen, Collin Meese, Mark Nejad, Chien-Chung Shen
First submitted to arxiv on: 17 Jul 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 The paper presents NeighborFL, a novel federated learning scheme for low-latency traffic prediction. Federated Learning for Traffic Prediction (FLTP) has gained popularity due to its ability to preserve privacy, reduce communication overhead, and improve prediction accuracy. However, most existing FLTP frameworks lack real-time model updating schemes, hindering their adaptability to changing traffic trends. NeighborFL addresses this limitation by introducing a personalized local models grouping heuristic based on haversine distance and error-driven approaches. This allows for location-aware and tailored prediction models for each client while promoting collaborative learning. The proposed scheme outperforms three baseline models in simulation experiments, showcasing its potential for real-time traffic prediction applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making smart city traffic management systems better by using a new way to learn from traffic data. Federated Learning is a technique that helps predict traffic patterns without revealing personal information. However, current methods don’t update their models quickly enough to adapt to changing traffic conditions. The proposed NeighborFL scheme solves this problem by creating personalized prediction models for each location based on distance and error calculations. This approach allows for more accurate real-time predictions and can be used in smart city traffic management systems. |
Keywords
» Artificial intelligence » Federated learning
