A differentially private distributed data mining scheme with high efficiency for edge computing

Abstract

A wide range of data mining applications benefit from the low latency offered by edge computing. However, edge computing suffers from limited computing resources, which inhibits the applications of the computationally expensive data mining methods. In the edge-cloud environment, usually, the participants turn to collaboratively train machine-learning models that yield more accurate prediction results. However, data owners may not be willing to sharing the own data for the privacy concerns. To handle such disparate goals, we focus on tree-based distributed data mining scheme with differential privacy, which is computationally friendly. The basic idea of our approach is based on a distributed ensemble strategy. Each participant builds an elegant decision model based on their own data, which has a good tradeoff between the computation and the accuracy of the data distribution, and shares it with other participants after being injected with the elaborate noise. Then the useful knowledge transferred from the decision models is acquired by other participants in an adaptive ensemble strategy. Both the theoretical analysis and the experiments show that our scheme provides an efficient data mining manner that can achieve a good prediction accuracy while providing rigorous privacy guarantee over the distributed data.

In-depth analysis and open challenges of Mist Computing

“…A clear example of this is federated learning in the Edge, which trains independent tiny models in the local networks in order to aggregate them later and produce an upgraded version that can be distributed efficiently along the entire network [90]. It goes one step further with completely distributed data mining systems where edge nodes share their individual trained models to improve setup and performance and preserving the privacy of the raw data [91]. • Dataflow programming: popular service architectures, such as SOA and Microservices, are usually too heavy for limited-resource nodes and lack flexibility in mobility aspects.…”

Optimal subsampling algorithm for composite quantile regression with distributed data

“…Qiu et al (2020)[7] constructed a data stream classification model based on distributed processing. Sun et al (2021)[10] proposed a data mining scheme for edge computing based on distributed integration strategy. Zhang and Wang (2021) [17] proposed a distributed subdata selection method for big data linear regression model.…”