Federated Machine Learning
Addo, Nancy (2020-02-27)
Federated Machine Learning
Addo, Nancy
(27.02.2020)
Julkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty.
avoin
Julkaisun pysyvä osoite on:
https://urn.fi/URN:NBN:fi-fe202003067497
https://urn.fi/URN:NBN:fi-fe202003067497
Tiivistelmä
In recent times, machine gaining knowledge has transformed areas such as processer visualisation, morphological and speech identification and processing. The implementation of machine learning is frim built on data and gathering the data in confidentiality disturbing circumstances. The studying of amalgamated systems and methods is an innovative area of modern technological field that facilitates the training within models without gathering the information. As an alternative to transferring the information, clients co-operate together to train a model be only delivering weights updates to the server. While this concerning privacy is better and more adaptable in some circumstances very expensive.
This thesis generally introduces some of the fundamental theories, structural design and procedures of federated machine learning and its prospective in numerous applications. Some optimisation methods and some privacy ensuring systems like differential privacy also reviewed.
This thesis generally introduces some of the fundamental theories, structural design and procedures of federated machine learning and its prospective in numerous applications. Some optimisation methods and some privacy ensuring systems like differential privacy also reviewed.