Efficient and Privacy-Enhanced Asynchronous Federated Learning for Multimedia Data in Edge-based IoT Article Swipe
Hu Xiong
,
Hang Yan
,
Mohammad S. Obaidat
,
Jingxue Chen
,
Mingsheng Cao
,
Sachin Kumar
,
Kadambri Agarwal
,
Saru Kumari
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1145/3688002
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1145/3688002
With the rapid development of smart device technology, the current version of the Internet of Things (IoT) is moving towards a multimedia IoT because of multimedia data. This innovative concept seamlessly integrates multimedia data with the IoT-Edge Continuum. Recently, a distributed learning framework shows promise in revolutionizing various industries, including smart cities, healthcare, etc. However, these applications may face challenges such as the presence of malicious devices that invade the privacy of other devices or corrupt uploaded model parameters. Additionally, the existing synchronous federated learning (FL) methods face challenges in effectively training models on local datasets due to the diversity of IoT devices. To tackle these concerns, we propose an efficient and privacy-enhanced asynchronous federated learning approach for multimedia data in edge-based IoT. In contrast to traditional FL methods, our approach combines revocable attribute-based encryption (RABE) and differential privacy (DP). This guarantees the privacy of the entire process while allowing seamless collaboration between multiple devices and the aggregation server during model training. Also, this combination brings a dynamic nature to the system. Furthermore, we utilize an asynchronous weight-based aggregation algorithm to improve the efficiency of training and the quality of the final returned model. Our proposed scheme is confirmed by theoretical safety proofs and experimental results with multimedia data. Performance evaluation shows that our framework reduces the cryptography runtime by 63.3% and the global model aggregation time by 61.9% compared to cutting-edge schemes. Moreover, our accuracy is comparable to the most primitive FL schemes, maintaining 86.7%, 70.8%, and 86.1% on MNIST, CIFAR-10, and Fashion-MNIST, respectively. The experimental results highlight the remarkable practicality, resilience and effectiveness of the proposed scheme.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1145/3688002
- https://dl.acm.org/doi/pdf/10.1145/3688002
- OA Status
- bronze
- Cited By
- 7
- References
- 53
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4401634624
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4401634624Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1145/3688002Digital Object Identifier
- Title
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Efficient and Privacy-Enhanced Asynchronous Federated Learning for Multimedia Data in Edge-based IoTWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-08-16Full publication date if available
- Authors
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Hu Xiong, Hang Yan, Mohammad S. Obaidat, Jingxue Chen, Mingsheng Cao, Sachin Kumar, Kadambri Agarwal, Saru KumariList of authors in order
- Landing page
-
https://doi.org/10.1145/3688002Publisher landing page
- PDF URL
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https://dl.acm.org/doi/pdf/10.1145/3688002Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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bronzeOpen access status per OpenAlex
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https://dl.acm.org/doi/pdf/10.1145/3688002Direct OA link when available
- Concepts
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Computer science, Asynchronous communication, Differential privacy, Upload, Enhanced Data Rates for GSM Evolution, Encryption, Multimedia, Edge device, Cryptography, Distributed computing, Computer network, Artificial intelligence, Computer security, Cloud computing, Data mining, World Wide Web, Operating systemTop concepts (fields/topics) attached by OpenAlex
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7Total citation count in OpenAlex
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2025: 6, 2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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