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Privacy-Preserving Machine Learning Using Federated Learning and Secure Aggregation

时间: 2024-05-22 15:10:37 浏览: 149

awesome-federated-learning:机器学习中有关联合学习和隐私的资源

出色的联合学习与机器学习中的联合学习和隐私相关的资源列表。相关真棒列表文件简介与调查联合学习中的进步和开放性问题联合机器学习：概念和应用联合学习系统：数据隐私和保护的愿景，炒作和现实揭开并行和分布式深度学习的神秘面纱：深度并发分析 EdgeAI：物联网时代的深度学习愿景高度分散和快速增长的数据的机器学习系统不偷看：私有分布式深度学习调查移动边缘网络中的联合学习：全面调查隐私权与安全性促进因果联合学习以增强鲁棒性和隐私性从去中心化数据对深层网络进行通信有效的学习https://arxiv.org/pdf/1602.05629.pdf 实用的安全聚合，可在用户掌握的数据上进行联合学习https://arxiv.org/abs/1611.04482 实用的安全聚合，用于保护隐私的机器学习https://storage.googleapis.com

Privacy-preserving machine learning is becoming increasingly important in today's world where data privacy is a major concern. Federated learning and secure aggregation are two techniques that can be used to achieve privacy-preserving machine learning. Federated learning is a technique where the machine learning model is trained on data that is distributed across multiple devices or servers. In this technique, the model is sent to the devices or servers, and the devices or servers perform the training locally on their own data. The trained model updates are then sent back to a central server, where they are aggregated to create a new version of the model. The key advantage of federated learning is that the data remains on the devices or servers, which helps to protect the privacy of the data. Secure aggregation is a technique that can be used to protect the privacy of the model updates that are sent to the central server. In this technique, the updates are encrypted before they are sent to the central server. The central server then performs the aggregation operation on the encrypted updates, and the result is sent back to the devices or servers. The devices or servers can then decrypt the result to obtain the updated model. By combining federated learning and secure aggregation, it is possible to achieve privacy-preserving machine learning. This approach allows for the training of machine learning models on sensitive data while protecting the privacy of the data and the model updates.

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