通过适应性声誉意识的联合学习和同型加密来增强保护隐私的脑瘤分类
Swetha Ghanta1, Prasanthi Boyapati1, Sujit Biswas2,3
1Department of Computer Science and Engineering, School of Engineering and Sciences, SRM University, AP, Guntur, Andhra Pradesh, India.
PeerJ. Computer science
|September 24, 2025
概括
联邦自适应声誉意识聚合与CKKS同型加密 (FedARCH) 提高了使用联合学习的大脑瘤诊断的准确性. 这种新的框架增强了对噪音数据的模型稳定性,并确保了医疗图像分析中的隐私.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 数据 隐私 数据 隐私 数据
背景情况:
- 通过MRI进行自动脑瘤诊断至关重要,但由于数据隐私和稀缺性而受到阻碍.
- 联合学习 (FL) 通过在不需要共享原始数据的情况下实现协作培训提供了一个解决方案,但它面临着自己的挑战.
- 现有的FL方法与数据异质性和隐私漏洞 (如模型倒置攻击) 相斗争.
研究的目的:
- 引入CKKS同型加密 (FedARCH) 的联邦自适应声誉意识聚合,这是一种用于跨筒医疗图像分析的新型FL框架.
- 为了提高全球模型的准确性和稳定性,对抗噪音数据和对抗性攻击.
- 在联合模型聚合过程中通过高效的同态加密来确保数据隐私.
主要方法:
- 开发了FedARCH,这是一个联合的学习框架,用于加权客户聚合的声誉评分.
- 集成的CKKS同型加密,用于对加密模型权重进行安全,保护隐私的操作.
- 实施了使用平滑和衰变因子进行自适应聚合的动态性能管理.
主要成果:
- 在区分脑瘤类别方面,FedARCH实现了99.39%的高精度.
- 该框架保持了94%的准确性,50%的噪音客户,明显优于标准FL (33%的准确性).
- 安全分析证实了FedARCH在减轻隐私风险和计算开销方面的有效性.
结论:
- 联邦ARCH为联合医疗图像分析提供了强大且保护隐私的解决方案.
- 拟议的声誉意识聚合和同态加密有效地解决了 FL 对医疗保健的关键挑战.
- 在现实世界的联邦环境中,FedARCH显示了提高脑瘤诊断准确性和可靠性的巨大潜力.
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