HomoPAI: A Secure Collaborative Machine Learning Platform based on Homomorphic Encryption

Moodle is a widely deployed distance learning platform that provides numerous opportunities to enhance the learning process. Moodle’s importance in maintaining the continuity of education in states of emergency and other circumstances has been particularly demonstrated in the context of the COVID-19 virus’ rapid spread. However, there is a problem with personalizing the learning and monitoring of students’ work. There is room for upgrading the system by applying data mining and different machine-learning methods. The multi-agent Observer system proposed in our paper supports students engaged in learning by monitoring their work and making suggestions based on the prediction of their final course success, using indicators of engagement and machine-learning algorithms. A novelty is that Observer collects data independently of the Moodle database, autonomously creates a training set, and learns from gathered data. Since the data are anonymized, researchers and lecturers can freely use them for purposes broader than that specified for Observer. The paper shows how the methodology, technologies, and techniques used in Observer provide an autonomous system of personalized assistance for students within Moodle platforms.

Download Full-text

Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning

Future Internet ◽

10.3390/fi13040094 ◽

2021 ◽

Vol 13 (4) ◽

pp. 94

Author(s):

Haokun Fang ◽

Quan Qian

Keyword(s):

Machine Learning ◽

Homomorphic Encryption ◽

Privacy Preserving ◽

Great Success ◽

Learning Framework ◽

Computational Overhead ◽

Important Concern ◽

Speed Up ◽

Key Length ◽

Core Idea

Privacy protection has been an important concern with the great success of machine learning. In this paper, it proposes a multi-party privacy preserving machine learning framework, named PFMLP, based on partially homomorphic encryption and federated learning. The core idea is all learning parties just transmitting the encrypted gradients by homomorphic encryption. From experiments, the model trained by PFMLP has almost the same accuracy, and the deviation is less than 1%. Considering the computational overhead of homomorphic encryption, we use an improved Paillier algorithm which can speed up the training by 25–28%. Moreover, comparisons on encryption key length, the learning network structure, number of learning clients, etc. are also discussed in detail in the paper.

Download Full-text

A machine learning platform for the discovery of materials

Journal of Cheminformatics ◽

10.1186/s13321-021-00518-y ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Carl E. Belle ◽

Vural Aksakalli ◽

Salvy P. Russo

Keyword(s):

Machine Learning ◽

Density Functional ◽

Gw Approximation ◽

Dft Methods ◽

Functional Theory ◽

Unit Cell Size ◽

Learning Platform ◽

Photovoltaic Materials ◽

Wide Range ◽

Quantum Espresso

AbstractFor photovoltaic materials, properties such as band gap $$E_{g}$$ E g are critical indicators of the material’s suitability to perform a desired function. Calculating $$E_{g}$$ E g is often performed using Density Functional Theory (DFT) methods, although more accurate calculation are performed using methods such as the GW approximation. DFT software often used to compute electronic properties includes applications such as VASP, CRYSTAL, CASTEP or Quantum Espresso. Depending on the unit cell size and symmetry of the material, these calculations can be computationally expensive. In this study, we present a new machine learning platform for the accurate prediction of properties such as $$E_{g}$$ E g of a wide range of materials.

Download Full-text