Privacy-Aware Resource Sharing in Cross-Device Federated Model Training for Collaborative Predictive Maintenance
Sourabh Bharti,Alan Mcgibney +1 more
TLDR
SplitPred as mentioned in this paper proposes a split-learning-based framework that enables FL clients to maximize available resources within their local network without compromising the benefits of a FL approach (i.e., privacy and shared learning).Abstract:
The proliferation of Industry 4.0 has made modern industrial assets a rich source of data that can be leveraged to optimise operations, ensure efficiency, and minimise maintenance costs. The availability of data is advantageous for asset management, however, attempts to maximise the value of this data often fall short due to additional constraints, such as privacy concerns and data stored in distributed silos that is difficult to access and share. Federated Learning (FL) has been explored to address these challenges and has been demonstrated to provide a mechanism that allows highly distributed data to be mined in a privacy-preserving manner and offering new opportunities for a collaborative approach to asset management. Despite the benefits, FL has some challenges that need to be overcome to make it fully compatible for asset management or more specifically predictive maintenance applications. FL requires a set of clients that participate in the model training process, however, orchestration, device heterogeneity and scalability can hinder the speed and accuracy in the context of collaborative predictive maintenance. To address this challenge, this work proposes a split-learning-based framework (SplitPred) that enables FL clients to maximise available resources within their local network without compromising the benefits of a FL approach (i.e., privacy and shared learning). Experiments performed on the benchmark C-MAPSS data-set demonstrate the advantage of applying SplitPred in the FL process in terms of efficient use of resources, i.e., model convergence time, accuracy, and network load.read more
Citations
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Journal ArticleDOI
Data mining in predictive maintenance systems: A taxonomy and systematic review
TL;DR: A systematic literature review that provides an overview of the current state of research concerning predictive maintenance from a data mining perspective and presents a first taxonomy that implies different phases considered in any data mining process to solve a predictive maintenance problem.
Journal ArticleDOI
Aggregation Strategy on Federated Machine Learning Algorithm for Collaborative Predictive Maintenance
Ali Bemani,Niclas Björsell +1 more
TL;DR: This study describes distributed ML for PM applications and proposes two federated algorithms: Federated support vector machine (FedSVM) with memory for anomaly detection and federated long-short term memory (FedLSTM) for remaining useful life (RUL) estimation that enables factories at the fog level to maximize their PM models’ accuracy without compromising their privacy.
Journal ArticleDOI
CoRoL: A Reliable Framework for Computation Offloading in Collaborative Robots
Sourabh Bharti,Alan McGibney +1 more
TL;DR: A reputation-based collaborative robotic learning (CoRoL) framework is proposed with the ability to isolate and/or minimize the impact of malicious or poor-performing cobots on computation task execution, supported by split learning for privacy-preserving task offloading with minimum data exchange.
Proceedings ArticleDOI
An Exhaustive Investigation on Resource-aware Client Selection Mechanisms for Cross-device Federated Learning
TL;DR: The effect of varying FL specific hyper-parameters on accuracy, convergence time and client retention is observed for all resource-aware client selection mechanisms so that a cognitive choice of the client selection mechanism can be made for a given application scenario.
Journal ArticleDOI
CoRoL: A Reliable Framework for Computation Offloading in Collaborative Robots
TL;DR: In this article , a reputation-based collaborative robotic learning (CoRoL) framework is proposed with the ability to isolate and/or minimize the impact of malicious or poor-performing robots on computation task execution.
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