Summary of Cabafl: Asynchronous Federated Learning Via Hierarchical Cache and Feature Balance, by Zeke Xia and Ming Hu and Dengke Yan and Xiaofei Xie and Tianlin Li and Anran Li and Junlong Zhou and Mingsong Chen
CaBaFL: Asynchronous Federated Learning via Hierarchical Cache and Feature Balance
by Zeke Xia, Ming Hu, Dengke Yan, Xiaofei Xie, Tianlin Li, Anran Li, Junlong Zhou, Mingsong Chen
First submitted to arxiv on: 19 Apr 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 A novel asynchronous Federated Learning (FL) approach named CaBaFL is proposed to address efficiency limitations in Artificial Intelligence of Things (AIoT) applications. CaBaFL incorporates a hierarchical Cache-based aggregation mechanism and a feature Balance-guided device selection strategy to mitigate stragglers and data imbalance issues. By maintaining multiple intermediate models, the approach enables local training on multiple devices to align training time and reduce straggler effects. The feature balance-guided device selection strategy ensures balanced data distributions for each intermediate model before aggregation. Compared to state-of-the-art FL methods, CaBaFL achieves up to 9.26X training acceleration and 19.71% accuracy improvements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CaBaFL is a new way of sharing information between many devices in the Artificial Intelligence of Things (AIoT). The problem is that some devices don’t finish learning at the same time, which slows things down. To fix this, CaBaFL uses a special system to train models on multiple devices together. It also makes sure all devices have the same amount of information, so everyone learns equally well. This new approach helps AIoT learn faster and better. |
Keywords
» Artificial intelligence » Federated learning
