Fog-Embedded Deep Learning for the Internet of Things
Abstract
In current deep learning models, centralized architecture forces participants to pool their data to the central Cloud to train a global model, while distributed architecture requires a parameter server to mediate the training process. However, privacy issues, response delays, and computation and communication bottlenecks prevent these architectures from working well at the scale of Internet of Things devices. To counter these problems, in this paper we build a Fog-embedded privacy-preserving deep learning framework (FPPDL), which moves computation from the centralized Cloud to Fog nodes near the end devices. The experimental results on benchmark image datasets under different settings demonstrate that FPPDL achieves comparable accuracy to the centralized stochastic gradient descent (SGD) framework, and delivers better accuracy than the standalone SGD framework. Our evaluations also show that both computation and communication cost are greatly reduced by FPPDL, hence achieving the desired tradeoff between privacy and performance.
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CAFE can recover large-scale private data from shared aggregated gradients in VFL settings, overcoming the batch limitation problem in current data leakage attacks.
“They empirically demonstrated that CAFE can recover large-scale private data from the shared aggregated gradients in VFL settings, overcoming the batch limitation problem in current data leakage attacks.”
From Privacy and Robustness in Federated Learning: Attacks and Defenses (2024) · cites this paper as [182]
CAFE uncovered the risk of catastrophic data leakage in VFL through a novel algorithm that can perform large-batch data leakage with high data recovery quality and theoretical guarantees.
“Another recent work [182] uncovered the risk of catastrophic data leakage in vertical federated learning (CAFE) through a novel algorithm that can perform largebatch data leakage with high data recovery quality and theoretical guarantees.”
From Privacy and Robustness in Federated Learning: Attacks and Defenses (2024) · cites this paper as [182]
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- Authors
- Lyu, L., Bezdek, J. C., He, X., & Jin, J.
- Affiliations
- Australian National University (AU)
- The University of Melbourne (AU)
- Monash University (AU)
- Swinburne University of Technology (AU)
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2019-07
- Volume
- 15
- Issue
- 7
- Pages
- 4206-4215
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W2941227905
- Cited by
- 61
- References
- 31
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 8 · 2023: 8 · 2022: 13 · 2021: 11 · 2020: 15 · 2019: 3
- FWCI
- 5.73
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Industrial Informatics (publishedVersion)
- Swinburne figshare (Swinburne University of Technology) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- fog computing, privacy-preserving deep learning, Internet of Things, distributed deep learning, federated learning, computation-communication trade-off, edge computing, image classification, computation cost reduction, communication cost reduction
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