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Updated: Sep 13, 2025

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Deep Neural Networks for Image-Based Dietary Assessment
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意识到异质性的个性化联合神经架构搜索
1College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China.
Entropy (Basel, Switzerland)
|July 29, 2025
概括
联合学习面临异质性挑战. 我们的HAPFNAS方法使用知识蒸和个性化预测器来改进神经架构,以寻找更好的个性化模型.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 分布式系统 分布式系统
背景情况:
- 联合学习 (FL) 允许在分散的设备上进行协作模式培训.
- 在FL的资源和统计异质性给模型培训和个性化带来了重大挑战.
- 神经架构搜索 (NAS) 可以优化模型,但与FL的异质性作斗争.
研究的目的:
- 提出一种新的方法,即具有异质意识的个性化联合NAS (HAPFNAS),以应对FL的挑战.
- 为了使高效和有效的个性化模型发现在异质的联邦环境中.
- 在联合设置中提高一拍NAS的稳定性和性能.
主要方法:
- 使用轻量级模型从客户端到服务器端超级网络实现知识蒸.
- 开发了基于随机森林的预测器,以进行高效,个性化的建筑性能评估.
- 引入了heteroFedAvg,一种模型异质的FL算法,用于对个性化模型进行协作训练.
主要成果:
- 哈普纳斯有效地缓解了联合NAS中的异质性问题.
- 提出的方法提高了超级网络培训的稳定性和架构评估的准确性.
- 与现有的联合NAS方法相比,CIFAR-10/100和Tiny-ImageNet的实验结果显示出更高的性能.
结论:
- 哈普纳斯成功地在异构的联合学习场景中实现了个性化的模型发现.
- 知识蒸和个性化预测器的整合是克服FL异质性的关键.
- 这项工作通过提供强大而有效的解决方案,推动了联合NAS领域的发展.
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