Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State-of-the-Art and Future Directions

Duan, Qiang; Hu, Silong; Deng, Ruijun; Lu, Zhihui

doi:10.3390/s22165983

Cited by 49 publications

(22 citation statements)

References 103 publications

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“…In an inference attack, the attacker aims to exchange gradients during the FL training process, which can result in serious leakage of information about clients' training data features. Inference attacks include inferring class representatives [66], inferring membership [67], inferring data properties [68], and inferring samples/labels [69]. In the inference of class representatives, the adversary creates samples that are not in the original training dataset.…”

Section: Privacy Leakage and Threats In Flmentioning

confidence: 99%

Privacy-Enhancing Technologies in Federated Learning for the Internet of Healthcare Things: A Survey

et al. 2023

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Advancements in wearable medical devices using the IoT technology are shaping the modern healthcare system. With the emergence of the Internet of Healthcare Things (IoHT), efficient healthcare services can be provided to patients. Healthcare professionals have effectively used AI-based models to analyze the data collected from IoHT devices to treat various diseases. Data must be processed and analyzed while avoiding privacy breaches, in compliance with legal rules and regulations, such as the HIPAA and GDPR. Federated learning (FL) is a machine learning-based approach allowing multiple entities to train an ML model collaboratively without sharing their data. It is particularly beneficial in healthcare, where data privacy and security are substantial concerns. Even though FL addresses some privacy concerns, there is still no formal proof of privacy guarantees for IoHT data. Privacy-enhancing technologies (PETs) are tools and techniques designed to enhance the privacy and security of online communications and data sharing. PETs provide a range of features that help protect users’ personal information and sensitive data from unauthorized access and tracking. This paper comprehensively reviews PETs concerning FL in the IoHT scenario and identifies several key challenges for future research.

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Section: Privacy Leakage and Threats In Flmentioning

confidence: 99%

Privacy-Enhancing Technologies in Federated Learning for the Internet of Healthcare Things: A Survey

et al. 2023

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“…In vanilla SL, the local model makes one or more training passes over the local dataset (alternative client training). This may cause "catastrophic forgetting" issue (Gawali et al, 2020;Duan et al, 2022). So SplitFedv3 propose to use alternate mini-batch training, where a client updates its client-side model on one mini-batch, after which the client next in order takes over (Gawali et al, 2020), to mitigate the issue.…”

Section: A Additional Related Workmentioning

confidence: 99%

“…FedSeq (Zaccone et al, 2022) is the same as SFLG except that the training inside the group is identical to vanilla SL. More variants can be found in ; Duan et al (2022).…”

Section: A Additional Related Workmentioning

confidence: 99%

Convergence Analysis of Split Learning on Non-IID Data

Li¹,

Lyu²

2023

Preprint

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Split Learning (SL) is one promising variant of Federated Learning (FL), where the AI model is split and trained at the clients and the server collaboratively. By offloading the computationintensive portions to the server, SL enables efficient model training on resource-constrained clients. Despite its booming applications, SL still lacks rigorous convergence analysis on non-IID data, which is critical for hyperparameter selection. In this paper, we first prove that SL exhibits an O(1/ √ R) convergence rate for non-convex objectives on non-IID data, where R is the number of total training rounds. The derived convergence results can facilitate understanding the effect of some crucial factors in SL (e.g., data heterogeneity and synchronization interval). Furthermore, comparing with the convergence result of FL, we show that the guarantee of SL is worse than FL in terms of training rounds on non-IID data. The experimental results verify our theory. More findings on the comparison between FL and SL in cross-device settings are also reported.

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“…Moreover, offloading training tasks from FL participant devices is essentially aligned with the emerging Split Learning (SL) paradigm [216]. Recently, encouraging progress has been made toward combining FL and SL in the same framework for improving learning performance [217], which opens another avenue to fully leverage edge computing for supporting collaborative learning. Therefore, the integration of FL and SL in an edge environment is expected to be an interesting topic for future research.…”

Section: Releasing Computation Loads From Fl Participant Devicesmentioning

confidence: 99%

Combining Federated Learning and Edge Computing toward Ubiquitous Intelligence: Challenges, Recent Advances, and Future Directions

Duan¹,

Huang²,

Hu³

et al. 2022

Preprint

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Full leverage of the huge volume of data generated on a large number of user devices for providing intelligent services in the 6G network calls for Ubiquitous Intelligence (UI). A key to developing UI lies in the involvement of the large number of network devices, which contribute their data to collaborative Machine Learning (ML) and provide their computational resources to support the learning process. Federated Learning (FL) is a new ML method that enables data owners to collaborate in model training without exposing private data, which allows user devices to contribute their data to developing UI. Edge computing deploys cloud-like capabilities at the network edge, which enables network devices to offer their computational resources for supporting FL. Therefore, a combination of FL and edge computing may greatly facilitate the development of ubiquitous intelligence. In this article, we present a comprehensive survey of the recent developments in technologies for combining FL and edge computing with a holistic vision across the fields of FL and edge computing. We conduct our survey from both the perspective of an FL framework deployed in an edge computing environment (FL in Edge) and the perspective of an edge computing system providing a platform for supporting FL (Edge for FL). From the FL in Edge perspective, we first identify the main challenges to FL in edge computing and then survey the representative technical strategies for addressing the challenges. From the Edge for FL perspective, we first analyze the key requirements for edge computing to support FL and then review the recent advances in edge computing technologies that may be exploited to meet the requirements. Then we discuss open problems and identify some possible directions for future research on combining FL and edge computing, with the hope of arousing the research community’s interest in this emerging and exciting interdisciplinary field.

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Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State-of-the-Art and Future Directions

Cited by 49 publications

References 103 publications

Privacy-Enhancing Technologies in Federated Learning for the Internet of Healthcare Things: A Survey

Privacy-Enhancing Technologies in Federated Learning for the Internet of Healthcare Things: A Survey

Convergence Analysis of Split Learning on Non-IID Data

Combining Federated Learning and Edge Computing toward Ubiquitous Intelligence: Challenges, Recent Advances, and Future Directions

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