Distributed additive encryption and quantization for privacy preserving federated deep learning
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TLDR
This work develops a practical, computationally efficient encryption based protocol for federated deep learning, where the key pairs are collaboratively generated without the help of a third party by quantization of the model parameters on the clients and an approximated aggregation on the server.About:
This article is published in Neurocomputing.The article was published on 2021-11-06 and is currently open access. It has received 21 citations till now. The article focuses on the topics: Encryption & Distributed key generation.read more
Citations
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Federated learning on non-IID data: A survey
TL;DR: In this article, a detailed analysis of the influence of non-IID data on both parametric and non-parametric machine learning models in both horizontal and vertical federated learning is provided.
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Application of Robust Zero-Watermarking Scheme Based on Federated Learning for Securing the Healthcare Data
TL;DR: In this article , a robust zero-watermarking scheme based on federated learning is proposed to solve the privacy and security issues of the teledermatology healthcare framework, which is suitable for the specific requirements of medical images, which neither changes the important information contained in medical images nor divulges privacy data.
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A federated data-driven evolutionary algorithm
TL;DR: In this paper, a federated data-driven evolutionary optimization framework that is able to perform data driven optimization when the data is distributed on multiple devices has been proposed, where a sorted model aggregation method is developed for aggregating local surrogates based on radial-basis function networks.
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Privacy-Preserving Aggregation in Federated Learning: A Survey
TL;DR: This survey aims to bridge the gap between a large number of studies on PPFL, where PPAgg is adopted to provide a privacy guarantee, and the lack of a comprehensive survey on the PPAGG protocols applied in FL systems.
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Practical Private Aggregation in Federated Learning Against Inference Attack
TL;DR: Wang et al. as mentioned in this paper proposed a federated learning framework, which can protect the data privacy of worker devices against the inference attacks with minimal accuracy cost and low computation and communication cost, and does not rely on the secure pairwise communication channels.
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TL;DR: Batch Normalization as mentioned in this paper normalizes layer inputs for each training mini-batch to reduce the internal covariate shift in deep neural networks, and achieves state-of-the-art performance on ImageNet.
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