Achieving Democracy in Edge Intelligence: A Fog-Based Collaborative Learning Scheme

Zhang, Tiehua; Shen, Zhishu; Jin, Jiong; Zheng, Xi; Tagami, Atsushi; Cao, Xianghui

doi:10.1109/jiot.2020.3020911

Cited by 32 publications

(13 citation statements)

References 31 publications

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“…To generate the adjacency matrix, four different node correlation functions are implemented and discussed separately. To evaluate the effectiveness of STFL, we follow the non-iid data setup (Zhang et al 2020) and split the different sleep stages to clients to verify the effectiveness of our proposed framework.…”

Section: Experimental Settingsmentioning

confidence: 99%

STFL: A Temporal-Spatial Federated Learning Framework for Graph Neural Networks

Lou¹,

Liu²,

Zhang³

et al. 2021

Preprint

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We present a spatial-temporal federated learning framework for graph neural networks, namely STFL. The framework explores the underlying correlation of the input spatial-temporal data and transform it to both node features and adjacency matrix. The federated learning setting in the framework ensures data privacy while achieving a good model generalization. Experiments results on the sleep stage dataset, ISRUC S3, illustrate the effectiveness of STFL on graph prediction tasks.

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Section: Experimental Settingsmentioning

confidence: 99%

STFL: A Temporal-Spatial Federated Learning Framework for Graph Neural Networks

Lou¹,

Liu²,

Zhang³

et al. 2021

Preprint

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“…The result is a collaborative learning scheme for DNNs in the edge with feedback of learned parameters to the cloud. Another solution is to remove the need for a cloud server entirely, by using a voting process to select an appropriate edge node as coordinator for a collaborative learning process [77]. The coordinator node is elected by all nodes through a democratic voting strategy, based on computational capacity and distance from the actual deployments.…”

Section: Enabling the Intelligent Edgementioning

confidence: 99%

Enabling and Leveraging AI in the Intelligent Edge: A Review of Current Trends and Future Directions

Goethals¹,

Volckaert

Turck

2021

IEEE Open J. Commun. Soc.

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The use of AI in Smart applications and in the organization of the network edge presents a rapidly advancing research field, with a great variety of challenges and opportunities. This article aims to provide a holistic review of studies from 2019 to 2021 related to the Intelligent Edge, a concept comprising both the use of AI to organize edge networks (Edge Intelligence) and Smart applications in the edge. An introduction is given to the technologies required to understand the state of the art of AI in edge networks, and a taxonomy is provided with "Enabling Technology" for Edge Intelligence, "Organization" of the edge using AI, and AI "Applications" in the edge as its main topics. Research trend data from 2015 to 2020 is presented for various subdivisions of these topics, showing both absolute and relative research interest in each subtopic. The "Organization" aspect, being the main focus of this article, has a more fine-grained subdivision, explaining all contributing factors in detail. The trends indicate an exponential increase in research interest in nearly all subtopics, but significant differences between them. For each subdivision of the taxonomy a number of selected studies from 2019 to 2021 are gathered to form a high-level illustration of the state of the art of Edge Intelligence. From these selected studies and the trend data, a number of short-term challenges and high-level visions for Edge Intelligence are formulated, providing a basis for future work.

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“…Recent work has also explored distributing the federated learning task across a hierarchical edge network [13] or selecting a coordinator from among a set of fog nodes [36]. These approaches are driven by a single coordinator and aim to learn a single global model.…”

Section: Introductionmentioning

confidence: 99%

Opportunistic Federated Learning: An Exploration of Egocentric Collaboration for Pervasive Computing Applications

Lee¹,

Zheng²,

Hua³

et al. 2021

2021 IEEE International Conference on Pervasive Computing and Communications (PerCom)

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Pervasive computing applications commonly involve user's personal smartphones collecting data to influence application behavior. Applications are often backed by models that learn from the user's experiences to provide personalized and responsive behavior. While models are often pre-trained on massive datasets, federated learning has gained attention for its ability to train globally shared models on users' private data without requiring the users to share their data directly. However, federated learning requires devices to collaborate via a central server, under the assumption that all users desire to learn the same model. We define a new approach, opportunistic federated learning, in which individual devices belonging to different users seek to learn robust models that are personalized to their user's own experiences. However, instead of learning in isolation, these models opportunistically incorporate the learned experiences of other devices they encounter opportunistically. In this paper, we explore the feasibility and limits of such an approach, culminating in a framework that supports encounter-based pairwise collaborative learning. The use of our opportunistic encounter-based learning amplifies the performance of personalized learning while resisting overfitting to encountered data.

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Achieving Democracy in Edge Intelligence: A Fog-Based Collaborative Learning Scheme

Cited by 32 publications

References 31 publications

STFL: A Temporal-Spatial Federated Learning Framework for Graph Neural Networks

STFL: A Temporal-Spatial Federated Learning Framework for Graph Neural Networks

Enabling and Leveraging AI in the Intelligent Edge: A Review of Current Trends and Future Directions

Opportunistic Federated Learning: An Exploration of Egocentric Collaboration for Pervasive Computing Applications

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