EdgeFed: Optimized Federated Learning Based on Edge Computing

Ye, Yunfan; Shen, Li; Liu, Fang; Tang, Yonghao; Hu, Wanting

doi:10.1109/access.2020.3038287

Cited by 130 publications

(61 citation statements)

References 21 publications

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“…There are some studies focused on federated edge computing. In [ 22 ], the authors proposed EdgeFed to apply local model updating to the edge server, which decreases computation cost and communication expense in the center data node. In [ 23 ], the authors designed a privacy-aware service placement (PSP) scheme in an edge-cloud system, which efficiently addressed individual privacy issues and provided better QoS to users.…”

Section: Related Workmentioning

confidence: 99%

Blockchain-Enabled Asynchronous Federated Learning in Edge Computing

Liu

et al. 2021

Sensors

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The fast proliferation of edge computing devices brings an increasing growth of data, which directly promotes machine learning (ML) technology development. However, privacy issues during data collection for ML tasks raise extensive concerns. To solve this issue, synchronous federated learning (FL) is proposed, which enables the central servers and end devices to maintain the same ML models by only exchanging model parameters. However, the diversity of computing power and data sizes leads to a significant difference in local training data consumption, and thereby causes the inefficiency of FL. Besides, the centralized processing of FL is vulnerable to single-point failure and poisoning attacks. Motivated by this, we propose an innovative method, federated learning with asynchronous convergence (FedAC) considering a staleness coefficient, while using a blockchain network instead of the classic central server to aggregate the global model. It avoids real-world issues such as interruption by abnormal local device training failure, dedicated attacks, etc. By comparing with the baseline models, we implement the proposed method on a real-world dataset, MNIST, and achieve accuracy rates of 98.96% and 95.84% in both horizontal and vertical FL modes, respectively. Extensive evaluation results show that FedAC outperforms most existing models.

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Section: Related Workmentioning

confidence: 99%

Blockchain-Enabled Asynchronous Federated Learning in Edge Computing

Liu

et al. 2021

Sensors

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“…However, such an idea where there is a middle entity in form of an edge server between sensing devices and the cloud is also gaining attention. The research work proposed in [108] is another effort based on a new hierarchical version of FL. In this, the edge serves as a local aggregator and the cloud as a global aggregator.…”

Section: Federated Learning and Iiotmentioning

confidence: 99%

“…In such a case, a local model update could report negative knowledge to the global aggregator as FL is based on data similarity of the participating clients [116]. However, a new version of FL in which a local server aggregates the data from IoT nodes and serves as a client to a global aggregator can help in overcoming such issues [108].…”

Section: ) Observations On Flmentioning

confidence: 99%

Edge Intelligence for Data Handling and Predictive Maintenance in IIOT

Hafeez

McArdle

2021

IEEE Access

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The use of IoT has become pervasive and IoT devices are common in many domains. Industrial IoT (IIoT) utilises IoT devices and sensors to monitor machines and environments to ensure optimal performance of equipment and processes. Predictive Maintenance (PM) which monitors the health of machines to determine the probable failure of components is one IIoT technique which is receiving attention lately. To achieve effective PM, massive amounts of data are collected, processed and ultimately analysed by Machine Learning (ML) algorithms. Traditionally IoT sensors transmit their data readings to the cloud for processing and modelling. Handling and transmitting massive amounts of data between IoT devices and infrastructure has a cost. Edge Computing (EC) in which both sensors and intermediate nodes can process data provides opportunities to reduce data transmission costs and increase processing speed. This article examines IIoT for PM and discusses how and where data can be processed and analysed. Initially, this article presents sampling and data reduction techniques. These techniques allow for a reduction in the amount of data transmitted to the cloud for processing but there are potential accuracy trade-offs when ML algorithms utilise reduced datasets. An alternative approach is to move ML algorithms closer to the data to reduce data transmission. There are three main techniques that utilise the EC paradigm to perform ML and data processing on intermediary nodes. These techniques are categorized according to where data processing occurs: Device and Edge, Edge and Cloud and Device and Cloud (Federated Learning). In addition to exploring traditional approaches, these three state-of-the-art techniques are examined in this article and their benefits and weaknesses are presented. A novel architecture to demonstrate how EC can be utilized both for data reduction and PM in IIoT is also proposed.

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“…), without exchanging data among the nodes of the network (i.e. maintaining the training data of each node locally, in a decentralized way) [44] [45] [46]. Especially the latter characteristic renders FL a by-definition privacy-aware method, which can reliably mitigate many of the systemic privacy risks and costs resulting from traditional centralized ML [47] [48] [49].…”

Section: Federated Learning Paradigmmentioning

confidence: 99%

Towards Open and Expandable Cognitive AI Architectures for Large-Scale Multi-Agent Human-Robot Collaborative Learning

2021

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Learning from Demonstration (LfD) constitutes one of the most robust methodologies for constructing efficient cognitive robotic systems. Despite the large body of research works already reported, current key technological challenges include those of multi-agent learning and long-term autonomy. Towards this direction, a novel cognitive architecture for multi-agent LfD robotic learning is introduced in this paper, targeting to enable the reliable deployment of open, scalable and expandable robotic systems in large-scale and complex environments. In particular, the designed architecture capitalizes on the recent advances in the Artificial Intelligence (AI) (and especially the Deep Learning (DL)) field, by establishing a Federated Learning (FL)-based framework for incarnating a multi-human multi-robot collaborative learning environment. The fundamental conceptualization relies on employing multiple AI-empowered cognitive processes (implementing various robotic tasks) that operate at the edge nodes of a network of robotic platforms, while global AI models (underpinning the aforementioned robotic tasks) are collectively created and shared among the network, by elegantly combining information from a large number of human-robot interaction instances. Regarding pivotal novelties, the designed cognitive architecture a) introduces a new FL-based formalism that extends the conventional LfD learning paradigm to support large-scale multi-agent operational settings, b) elaborates previous FL-based self-learning robotic schemes so as to incorporate the human in the learning loop and c) consolidates the fundamental principles of FL with additional sophisticated AI-enabled learning methodologies for modelling the multi-level inter-dependencies among the robotic tasks. The applicability of the proposed framework is explained using an example of a realworld industrial case study (subject to ongoing research activities) for agile production-based Critical Raw Materials (CRM) recovery. INDEX TERMSLearning from demonstration, human-robot interaction, artificial intelligence, federated learning VOLUME XX, XXXX This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.

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EdgeFed: Optimized Federated Learning Based on Edge Computing

Cited by 130 publications

References 21 publications

Blockchain-Enabled Asynchronous Federated Learning in Edge Computing

Blockchain-Enabled Asynchronous Federated Learning in Edge Computing

Edge Intelligence for Data Handling and Predictive Maintenance in IIOT

Towards Open and Expandable Cognitive AI Architectures for Large-Scale Multi-Agent Human-Robot Collaborative Learning

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