… PADL, a Privacy-aware and Asynchronous Deep Learning framework which enables multiple
data collecting sites to collaboratively train Deep … an Advanced Asynchronous Optimization …

… Lu, “PADL: Privacy-aware and asynchronous deep learning for IoT applications,” IEEE
Internet Things J., early access, Mar. 17, 2020, doi: 10.1109/JIOT.2020.2981379…

… Federated learning (FL), as a distributed machine learning paradigm, essentially promises …
in DP applications. In this paper, we propose a fine-grained and privacy-aware FL framework (…

… -preserving machine learning models. Abadi et al. [27] presented a DP-based deep learning
… [28] proposed privacy-aware deep learning for IoT applications via differential privacy with …

… is encrypted using a one-time pad that comprises a Merkle root. … attaches a payload to the
Tangle asynchronously (Line: 7). … direction is to leverage machine learning algorithms that are …

… Implement procedure for the FL-based application As the FL can solve data barrier issues,
it can be utilized to develop deep learning-based applications, which mainly involves five …

… learning to ensure fairness. To protect gradients transaction during privacy-preserving
collaborative deep learning… , privacy and fairness in collaborative deep learning can be effectively …

… of deep learning-based models [5]–[7]. However, due to privacy concerns, traditional centralized
… Lu, “PADL: Privacy-aware and asynchronous deep learning for IoT applications,” IEEE …

… [57] proposed privacy-aware and asynchronous DLassisted IoT applications (PADL). This
… processing big data streams in IoT applications. The review of characteristics reveals that …

… bottleneck of traditional machine learning on data collection and … This type of federated
learning is commonly adopted in IoT … privacy-preserving asynchronous federated learning that pre…