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Memory-efficient DRASiW Models
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DOI：10.1016/j.neucom.2024.128443.png)
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Abstract
En 中文
Weightless Neural Networks (WNN) are ideal for Federated Learning due to their robustness and computational efficiency. These scenarios require models with a small memory footprint and the ability to aggregate knowledge from multiple models. In this work, we demonstrate the effectiveness of using Bloom filter variations to implement DRASiW models-an adaptation of WNN that records both the presence and frequency of patterns-with minimized memory usage. Across various datasets, DRASiW models show competitive performance compared to models like Random Forest, k-Nearest Neighbors, Multi-layer Perceptron, and Support Vector Machines, with an acceptable space trade-off. Furthermore, our findings indicate that Bloom filter variations, such as Count Min Sketch, can reduce the memory footprint of DRASiW models by up to 27% while maintaining performance and enabling distributed and federated learning strategies.
Keywords:
Bloom filters
DRASiW
Knowledge aggregation
Weightless neural network
Journal
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6.5
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2.5W
Citations:
6.5W