Secure Model Fusion for Distributed Learning Using Partial Homomorphic Encryption-Reference-Cited by-同舟云学术

Secure Model Fusion for Distributed Learning Using Partial Homomorphic Encryption

Published:2019 Issue: Volume: Page:154-179
ISSN:0302-9743
Container-title:Policy-Based Autonomic Data Governance
language:
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Author:

Liu Changchang,Chakraborty Supriyo,Verma Dinesh

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-17277-0_9

Reference20 articles.

1. McMahan, H.B., Moore, E., Ramage, D., Hampson, S.: Communication-efficient learning of deep networks from decentralized data. arXiv preprint arXiv:1602.05629 (2016)

2. Bonawitz, K., et al.: Practical secure aggregation for federated learning on user-held data. arXiv preprint arXiv:1611.04482 (2016)

3. Verma, D., Julier, S., Cirincione, G.: Federated AI for building AI solutions across multiple agencies. In: AAAI FSS-18: Artificial Intelligence in Government and Public Sector, Arlington, VA, USA (2018)

4. Wang, S., et al.: When edge meets learning: adaptive control for resource-constrained distributed machine learning. In: IEEE International Conference on Computer Communications (2018)

5. Verma, D., Chakraborty, S., Calo, S., Julier, S., Pasteris, S.: An algorithm for model fusion for distributed learning. In: Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR IX, vol. 10635, p. 106350O. International Society for Optics and Photonics (2018)

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