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You searched for subject:(Federater learning). Showing records 1 – 2 of 2 total matches.

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Delft University of Technology

1. Enthoven, David (author). Privacy in federated deep learning on medical data.

Degree: 2019, Delft University of Technology

With the increasing number of data collectors such as smartphones, immense amounts of data are available. These data have great value for training machine learning models. Federated learning is a distributed machine learning approach that allows a machine learning model to train on a distributed data-set without transferring any data and therefore claims that privacy is preserved. In this thesis, privacy is considered specifically for the use-case of medical data. These are sensitive and distinct for different patients. A step-wise argument as to what constitutes privacy preservation is formulated. This notably requires systems to be able to train on singular samples without compromising their privacy. As such, the federated averaging algorithm (FedAvg) is demonstrated to be critically insecure against certain attack methods. A chosen attack method is used to show how training data is reconstructed with solely the model update. The viability of this attack method is demonstrated to great extend for fully connected neural networks and convolutional neural networks To adhere to the strict privacy formulation, a novel federated learning method is presented in this thesis which is called Locally Encoded Federated Averaging (LEFedAvg). This method works on the premise that a part of the model remains private throughout. Subsequently, it is demonstrated to be usable and how this method allows for collaborative training. The privacy benefits of this federated learning method are empirically shown. The trade-off between performance and privacy is demonstrated and discussed for a more realistic operational setting.

Electrical Engineer | Embedded Systems

Advisors/Committee Members: Al-Ars, Zaid (mentor), Delft University of Technology (degree granting institution).

Subjects/Keywords: Federater learning; Deep learning; privacy; Model sharing

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Enthoven, D. (. (2019). Privacy in federated deep learning on medical data. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:a6f05abc-fe60-446d-a0fc-a1818edd25e2

Chicago Manual of Style (16th Edition):

Enthoven, David (author). “Privacy in federated deep learning on medical data.” 2019. Masters Thesis, Delft University of Technology. Accessed January 19, 2021. http://resolver.tudelft.nl/uuid:a6f05abc-fe60-446d-a0fc-a1818edd25e2.

MLA Handbook (7th Edition):

Enthoven, David (author). “Privacy in federated deep learning on medical data.” 2019. Web. 19 Jan 2021.

Vancouver:

Enthoven D(. Privacy in federated deep learning on medical data. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Jan 19]. Available from: http://resolver.tudelft.nl/uuid:a6f05abc-fe60-446d-a0fc-a1818edd25e2.

Council of Science Editors:

Enthoven D(. Privacy in federated deep learning on medical data. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:a6f05abc-fe60-446d-a0fc-a1818edd25e2


Delft University of Technology

2. Hofman, Stefan (author). Federated Learning for Mobile and Embedded Systems.

Degree: 2020, Delft University of Technology

An increase in the performance of mobile devices has started a revolution in deploying artificial intelligence (AI) algorithms on mobile and embedded systems. In addition, fueled by the need for privacy-aware insights into data, we see a strong push towards federated machine learning, where data is stored locally and not shared with a central server. By allowing data to stay on client devices and do training locally, we work towards a more privacy-friendly future. Furthermore, utilizing federated machine learning enables machine learning in data-constrained environments where bandwidth is not sufficient to upload the entire dataset. In this thesis, we look at the recent trend into less complex machine learning models. These models optimize resource usage while reducing accuracy loss. We investigate how these simpler models hold up within a federated setting. We also look into the developments of AI frameworks and their capabilities for mobile platforms. Based on these findings, we propose that model-hyper-parameter optimization is possible to maximize accuracy for smaller networks during federated learning. We show that it is possible to reduce the accuracy loss from 15% to only 0.04%. We then demonstrate what a mobile implementation looks like and the performance we see from an iPhone X. We show that an iPhone implementation takes less than 2x the amount of a regular laptop implementation. Finally, we demonstrate that we can reduce the model-size by up to 7x using modern weight quantization methods. Advisors/Committee Members: Al-Ars, Z. (mentor), van Leuken, T.G.R.M. (graduation committee), Hoozemans, J.J. (graduation committee), Delft University of Technology (degree granting institution).

Subjects/Keywords: Federater learning; Artificial Intelligence; MobileNetV2; Embedded Systems; CoreML; On-device Learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Hofman, S. (. (2020). Federated Learning for Mobile and Embedded Systems. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:505e7f5b-a78d-444f-a92e-a88bcc7c08d0

Chicago Manual of Style (16th Edition):

Hofman, Stefan (author). “Federated Learning for Mobile and Embedded Systems.” 2020. Masters Thesis, Delft University of Technology. Accessed January 19, 2021. http://resolver.tudelft.nl/uuid:505e7f5b-a78d-444f-a92e-a88bcc7c08d0.

MLA Handbook (7th Edition):

Hofman, Stefan (author). “Federated Learning for Mobile and Embedded Systems.” 2020. Web. 19 Jan 2021.

Vancouver:

Hofman S(. Federated Learning for Mobile and Embedded Systems. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2021 Jan 19]. Available from: http://resolver.tudelft.nl/uuid:505e7f5b-a78d-444f-a92e-a88bcc7c08d0.

Council of Science Editors:

Hofman S(. Federated Learning for Mobile and Embedded Systems. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:505e7f5b-a78d-444f-a92e-a88bcc7c08d0

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