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相关实验视频

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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FedTKD:基于适应性知识蒸的可靠的异质联合学习.

Leiming Chen1, Weishan Zhang1, Cihao Dong1

  • 1School of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China.

Entropy (Basel, Switzerland)
|January 26, 2024
PubMed
概括

本研究介绍了FedTKD,这是一个可靠的,用于异质模型的联合学习框架. 它可以识别恶意客户端,并选择性地融合知识,在不同的环境中提高模型准确性和隐私.

关键词:
适应性的知识蒸.不同质的联合学习.恶意客户端识别恶意客户端识别值得信赖的知识聚合,可靠的知识聚合.

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科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 保护隐私的技术 保护隐私的技术

背景情况:

  • 传统的联合学习 (FL) 需要同质的模型结构,限制其在现实世界异质环境中的应用.
  • 现有的异质FL知识蒸方法通常假定客户可信度,未能解决恶意或低质量的数据贡献.
  • 在FL中整合个性化模型是具有挑战性的,因为模型异质性和需要可靠的知识聚合.

研究的目的:

  • 提出一个值得信赖的异质联合学习框架 (FedTKD),解决客户识别和可靠的知识融合问题.
  • 在具有多样化的客户端模型结构和潜在恶意参与者的环境中实现联合学习.
  • 在异质条件下提高联合模型的准确性和稳定性.

主要方法:

  • 开发了一种恶意客户端识别方法,使用客户端logit功能来过不可靠的信息.
  • 实施了选择性知识融合技术,用于高质量的全球逻辑计算.
  • 引入了适应性知识蒸方法,以改善服务器对客户端的知识传输.

主要成果:

  • 与基线方法相比,FedTKD在各种攻击和数据分布场景中表现出优越的性能.
  • 该框架在不同的攻击策略下也表现出稳定的性能.
  • 在具有异质数据分布的联合模型中实现了2%至3%的准确性改进.

结论:

  • FedTKD有效地解决了在异构的联合学习环境中可靠的知识融合的挑战.
  • 提出的客户身份识别和选择性知识聚合方法提高了模型可靠性和隐私.
  • 该框架为与多样化且可能不值得信赖的客户的实用联合学习应用提供了强大的解决方案.