Federated learning (FL) is an attractive solution which holds promise to efficiently realize intelligent privacy‐preserving IoT systems. It does so by training local models at the IoT devices using locally collected data and aggregating these models at the server. First, user privacy is achieved as no data is shared with the server. Second, the traffic generated reduces significantly as only the model updates are exchanged between the server and the devices. The first part of this study examines the validity of using FL for provisioning user data privacy in the context of missing data inference in environmental MCS. Using a representative machine language (ML) model, namely, neural network (NN), it is found through simulations with the help of an existing dataset that FL performs as good as traditional ML, while maintaining user privacy. The second part of the study is the design of a privacy‐aware framework for FL sharing (sharing of model updates) so as to ensure privacy of model updates. The proposed framework is economical w.r.t. computational and communication overhead as it uses: (a) a single asymmetric key shared among the FL clients with the help of threshold cryptography, and (b) partial homomorphic encryption (PHE) for sharing the model updates. Additionally, security analysis of the framework supports its validity.

中文翻译：

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用于环境人群感知中缺失数据推理的联邦学习隐私框架

联邦学习（FL）是一种有吸引力的解决方案，有望有效实现智能隐私保护物联网系统。它通过使用本地收集的数据在物联网设备上训练本地模型并在服务器上聚合这些模型来实现这一点。首先，由于不与服务器共享数据，因此实现了用户隐私。其次，由于服务器和设备之间仅交换模型更新，因此产生的流量显着减少。本研究的第一部分研究了在环境 MCS 中缺失数据推断的情况下使用 FL 提供用户数据隐私的有效性。使用代表性的机器语言（ML）模型，即神经网络（NN），通过借助现有数据集进行模拟发现，FL 的性能与传统 ML 一样好，同时维护了用户隐私。研究的第二部分是设计一个用于 FL 共享（共享模型更新）的隐私感知框架，以确保模型更新的隐私。所提出的框架对于计算和通信开销来说是经济的，因为它使用：（a）借助阈值密码学在 FL 客户端之间共享的单个非对称密钥，以及（b）用于共享模型更新的部分同态加密（PHE）。此外，框架的安全分析支持其有效性。