Federated Learning

Assembly Line

Decentralized learning and intelligent automation: the key to zero-touch networks?

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✍️ Authors: Selim Ickin, Hannes Larsson, Hassam Riaz, Xiaoyu Lan, Caner Kilinc

πŸ”– Topics: AI, Machine Learning, Federated Learning

Decentralized learning and the multi-armed bandit agent… It may sound like the sci-fi version of an old western. But could this dynamic duo hold the key to efficient distributed machine learning – a crucial factor in the realization of zero-touch automated mobile networks? Let’s find out.

Next-generation autonomous mobile networks will be complex ecosystems made up of a massive number of decentralized and intelligent network devices and nodes – network elements that may be both producing and consuming data simultaneously. If we are to realize our goal of fully automated zero-touch networks, new models of training artificial intelligence (AI) models need to be developed to accommodate these complex and diverse ecosystems.

Read more at Ericsson Blog

Efficient federated convolutional neural network with information fusion for rolling bearing fault diagnosis

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✍️ Authors: Zehui Zhang, Xiaobin Xu, Wenfeng Gong, Yuwang Chen, Haibo Gao

πŸ”– Topics: Bearing, Federated Learning, Convolutional Neural Network, Machine Health

🏒 Organizations: Wuhan University of Technology, Hangzhou Dianzi University, The University of Manchester

In the past year, various deep learning-based fault diagnosis methods have been designed to guarantee the stable, safe, and efficient operation of electromechanical systems. To achieve excellent diagnostic performance, the conventional centralized learning (CL) approach is adopted to collect as much data as possible from multiple industrial participants for deep model training. Due to privacy concerns and potential conflicts, industrial participants are unwilling to share their data resources. To solve the issues, this study proposes a fault diagnosis method based on federated learning (FL) and convolutional neural network (CNN), which allows different industrial participants to collaboratively train a global fault diagnosis model without sharing their local data. Model training is locally executed within each industrial participant, and the cloud server updates the global model by aggregating the local models of the participants. Specifically, an adaptive method is designed to adjust the model aggregation interval according to the feedback information of the industrial participants in order to reduce the communication cost while ensuring model accuracy. In addition, momentum gradient descent (MGD) and dropout layer are used to accelerate convergence rate and avoid model overfitting, respectively. The effectiveness of the proposed method is verified on a non-independent and identically distributed (non-iid) rolling bearing fault dataset. The experiment results indicate that the proposed method has higher accuracy than traditional fault diagnosis methods. Moreover, this study provides a promising collaborative training approach to the fault diagnosis field.

Read more at Control Engineering Practice

Deep Learning Boosts Robotic Picking Flexibility

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πŸ”– Topics: robotics, federated learning

🏒 Organizations: Festo, Karlsruhe Institute of Technology, DarwinAI

Gripping and manipulating items of diverse shapes and sizes has long been one of the biggest challenges facing industrial robotics. The difficulty is perhaps best summed up by the Polanyi Paradox, which states that we β€œknow more than we can tell.” In essence, while it may be easy to teach machines to exhibit a high level of performance on tasks that require abstract reasoning such as running computations, it is substantially harder to grant them the sensory-motor skills of even a small child in all but the most standardized and predictable environments.

Read more at Automation World