To optimize machine performance, companies often require real-time data analysis across multiple facilities. However, challenges like data privacy concerns, the vast amount of data, and limited connectivity can prohibit data exchange across facilities.

Federated Learning offers an innovative solution by enabling collaborative learning from distributed data without centralization. This approach effectively enhances machine performance, preserves data privacy, and promotes efficient knowledge sharing among decentralized participants. In this article, we delve into the fundamentals of federated learning, its functionality, and real-world success stories.

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What is Federated Learning?

Federated Learning is a machine learning (ML) approach, also known as collaborative learning, which allows models to be trained collaboratively across decentralized devices or data sources while keeping the raw data localized and private.  

It was first introduced in 2017 by researchers at Google as a method to collaboratively train a neural network model across smartphones without exposing local user data.  

In traditional ML, data is often centralized on a server or in the cloud for model training, posing privacy and security risks. On the other hand, federated learning trains models collaboratively across decentralized while keeping data local and confidential.

The basic concept of federated learning revolves around deploying a unified model to decentralized devices that is processing and updated by the local data. Only the updated model parameters are shared with the central instance, while private data stays on the device.

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How Federated Learning Works?

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At its core, federated learning involves three key components - the central server, the clients (devices or data sources), and the ML model. The process typically unfolds as follows:  

1. Local model training: ML models are trained at the clients (in the local environment on the local data), and then summarize the main change as an update.

2. Model centralization & fusion: For each distributed client, only the updated model parameters are transmitted to the central server (in the cloud) via encrypted channels. Here, the updated model is fused with updates from other clients to enhance the overall collaborative model.

3. Model redistribution: The fused model is redistributed and individualized on the local data. Then another cycle is started until the desired model performance is reached.  

4. Model deployment: The training data remains exclusively on the client, with no individual updates being retained within the cloud.

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What Are the Common Applications in Industries?  

Here are common applications of federated learning in different industries:

1. Manufacturing: Manufacturing can use federated learning to collaborate and refine machine models across plants without sharing sensitive machine data, enabling predictive maintenance and improved quality control.

2. Energy: Federated learning allows manufacturing plants to analyze energy consumption patterns across separate locations or production lines. By pooling data from multiple sources, the plants can develop algorithms that optimize energy usage based on real-time demand, production requirements, and external factors like weather conditions.

3. Autonomous Vehicles: Companies can use federated learning to improve their models by aggregating data from vehicles on the road without exposing location or route information.

4. Healthcare: Federated learning ensures privacy compliance by enabling hospitals and research institutions to train predictive disease models to serve clinical research without compromising patient data. Similarly, in health monitoring, collaboration between personal health devices such as wearables and smart watches can enhance health prediction models without the need to share personal health records.

5. Smart City: Federated learning can be used to optimize traffic management and reduce congestion by getting traffic data from multiple sensors and cameras.  

Why Use Federated Learning in Manufacturing?

To address issues such as data privacy dilemmas, diverse demands, and intermittent internet connectivity, federated learning holds significant importance in the manufacturing industry, including:  

1. Multi-Site Collaboration and Real-Time Insights: Federated learning is ideal for companies with multiple facilities spread across various locations, as it allows them to collectively enhance models for quality control, predictive maintenance, and process optimization without centralizing data. This not only improves decision-making, and reduces data transfer costs and security risks, but is particularly valuable in IoT environments.

2. Efficiency and Scalability: Federated Learning revolutionizes the training process by utilizing client device resources, reducing data transfer needs, and central server processing. This enhances efficiency, reduces bandwidth requirements, and scales well to accommodate a large number of clients.  

3. Regulatory Compliance: For businesses operating in regions with stringent data protection regulations, such as GDPR in Europe, federated learning offers a compliance-friendly approach by keeping data localized and minimizing cross-border data transfers.

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How prenode Uses Federated Learning  

prenode uses federated learning as a core methodology for our AI/ML projects. It can integrate with current IT infrastructures, facilitating secure asynchronous communication between diverse entities. We have achieved success with federated learning across a range of industrial applications, including predictive maintenance, smart monitoring, remote operations, and healthcare.  

Below are examples of how we use federated learning:

• Predicting the sorting time of sheet metal parts to improve production planning with the TRUMPF Sorting Guide: The Sorting Guide assists in sorting laser-cut sheet metal parts manually. The confidential part specifications cannot be shared with any third party of even TRUMPF. To allow learning across different installations, we use federated learning to train and deploy models locally on edge devices to predict sorting time, thereby optimizing production scheduling and reducing downtime.
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• Classifying ZEISS OCT scans of the retina to support medical decisions: Medical data is highly sensitive, and its centralization is often not possible. In collaboration with researchers from the ZEISS Group and Carl Zeiss Meditec AG, we applied our decentralized approach to detect anomalies in OCT images of eyes.
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• Predicting errors on WEISSER precision turning machines to increase machine availability: We use distributed data sources to predict maintenance events for WEISSER machines. The different ML models will learn from each other so that failures can be predicted with higher precision. Federated learning can overcome the issue that failures on individual machines occur infrequently.

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In conclusion  

In contrast to centralized learning, federated learning offers substantial privacy improvements without compromising model accuracy. By combining machine data decentralization, Edge AI, and the power of federated learning, industries can enhance their predictive maintenance, optimize processes, and increase scalability and cost-efficiency. It has proven to be a robust, scalable, and production-ready strategy, as is exemplified by its adoption by industry giants like Google. The benefits companies gain from adopting federated learning far outweigh the associated technical challenges, fostering digital transformation and Industry 4.0.  

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