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Combining Split and Federated Architectures for Efficiency and Privacy in Deep Learning

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Combining Split and Federated Architectures for Efficiency and Privacy in Deep Learning.

Rel. Guido Marchetto, Flavio Esposito. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Matematica, 2020

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Mobile phones and wearable devices are used and carried everywhere by people, producing every day a huge amount of distributed and sensitive data. Standard machine learning techniques need to collect this kind of data all on the same machine to compute the training and obtain a model useful to make predictions. To better preserve the privacy of data and, at the same time, guarantee a comparable performance, Federated and Split Learning have been designed. This work tries to improve the efficiency and privacy of Split and Federated learning combining them in two different types of architectures using the PySyft library implemented inside PyTorch. The code has been developed to easily switch from local to remote learning, simulating the distributed process inside the same machine and, then, running the neural network on workers located on different devices. Then, a trade-off analysis between the two architectures in terms of efficiency and privacy was performed: changing the distribution of data inside each device, estimating the possibility to rebuild the original data using the neural network inversion attack, and exploring possible privacy improvements. Finally, the results encourage us to further investigate the architectures and find which ones are more suitable to maximize both the performance and privacy, according to the data.

Relators: Guido Marchetto, Flavio Esposito
Academic year: 2020/21
Publication type: Electronic
Number of Pages: 70
Corso di laurea: Corso di laurea magistrale in Ingegneria Matematica
Classe di laurea: New organization > Master science > LM-44 - MATHEMATICAL MODELLING FOR ENGINEERING
Ente in cotutela: Saint Louis University in St. Louis, Missouri, United States (STATI UNITI D'AMERICA)
Aziende collaboratrici: Saint Louis University
URI: http://webthesis.biblio.polito.it/id/eprint/15597
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