Summary of Handling Spatial-temporal Data Heterogeneity For Federated Continual Learning Via Tail Anchor, by Hao Yu et al.
Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor
by Hao Yu, Xin Yang, Le Zhang, Hanlin Gu, Tianrui Li, Lixin Fan, Qiang Yang
First submitted to arxiv on: 24 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 Federated Tail Anchor (FedTA) approach aims to address the challenges of federated continual learning (FCL) by mitigating spatial-temporal catastrophic forgetting. By mixing trainable Tail Anchors with frozen output features, FedTA adjusts their position in the feature space to overcome parameter-forgetting and output-forgetting. This is achieved through three novel components: Input Enhancement for improving pre-trained model performance on downstream tasks; Selective Input Knowledge Fusion for fusing heterogeneous local knowledge on the server; and Best Global Prototype Selection for finding the best anchor point for each class in the feature space. FedTA outperforms existing FCL methods while effectively preserving relative feature positions, demonstrating its potential to enhance federated learning applicability in real-world scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated continual learning helps devices learn from different tasks. However, this type of learning is affected by data differences between devices and tasks. This paper shows that these differences can cause the model to forget important information. To solve this problem, researchers propose an approach called Federated Tail Anchor (FedTA). FedTA combines two parts: trainable anchors and frozen output features. This combination helps adjust the position of the anchors in the feature space, preventing the model from forgetting important information. The paper also presents three new components that help improve the performance of the model. These components include Input Enhancement for improving pre-trained models, Selective Input Knowledge Fusion for fusing different local knowledge on the server, and Best Global Prototype Selection for finding the best anchor point. The results show that FedTA outperforms existing methods and effectively preserves important information. |
Keywords
» Artificial intelligence » Continual learning » Federated learning
