Abstract
Traditionally, deep learning practitioners would bring data into a central repository for model training and inference. Recent developments in distributed learning, such as federated learning and deep learning as a service (DLaaS), do not require centralized data and instead push computing to where the distributed datasets reside. These decentralized training schemes, however, introduce additional security and privacy challenges. This survey first structures the field of distributed learning into two main paradigms and then provides an overview of the recently published protective measures for each. This work highlights both secure training methods as well as private inference measures. Our analyses show that recent publications, while being highly dependent on the problem definition, report progress in terms of security, privacy, and efficiency. Nevertheless, we also identify several current issues within the private and secure distributed deep learning (PSDDL) field that require more research. We discuss these issues and provide a general overview of how they might be resolved.
Original language | English |
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Article number | 3703452 |
Pages (from-to) | 1-43 |
Number of pages | 43 |
Journal | ACM Computing Surveys |
Volume | 57 |
Issue number | 4 |
Early online date | 9 Dec 2024 |
DOIs | |
Publication status | E-pub ahead of print - 9 Dec 2024 |
Bibliographical note
Publisher Copyright:© 2024 Copyright held by the owner/author(s).
Keywords
- Deep learning
- distributed learning
- privacy
- security