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Understanding Fusion Learning: The One Shot Federated Learning
In this article we will learn about Fusion learning and get to know how its working, its advantages and all other parameters. As technology grows we are getting more concerned about privacy in the field of machine learning. Earlier we used to train the data in centralized form which is more vulnerable to privacy so we are shifting towards Federated learning which allows us to train models by collaborating and without sharing the raw data which is a good technique in terms of privacy. Let’s get to know about Federated Learning.
Federated Learning
This is a decentralized mechanism of machine learning where we perform the model training locally into individual devices and after the training of data each device will share the updates about the model with the centralized server. This server will take out all the updates and make it to train the global model. So, using this technique data will be into the device and device will exchange only the model parameter with the central server. But this requires multiple number of communication between the device and the central server which leads to high cost and time.
Fusion Learning
This is also called as The One Shot Federated Learning. Fusion Learning is a new approach to federated learning that allows which is used to tackle the challenges and drawbacks of Federated learning. It combines the strengths of federated learning with knowledge distillation techniques. So, where Federated learning was using multiple rounds of communication, Fusion learning will only take one round of communication between the device and the central server.
Working of Fusion Learning
The central server first initializes the global model which is pre-trained on large dataset.
Each device separately trains a local model based on its own data and then as discussed above the local device models parameters are compressed into a compact representation using the first working method also called knowledge distillation and then it is sent to the central server.
Central server then collect the updates from all the devices using various aggregation techniques like weighted averaging and then it updates the global model.
After all the steps have completed then the updated global model will be broadcasted back to all devices.
Fusion Learning has Many Advantages Compared to Federated Learning
We can use the fusion learning with the device which is having limited storage or computational resources.
Improved Performance − Fusion Learning allows devices to use the global model's knowledge which has added the improvement in model accuracy.
Fusion learning is more secure as it is an advancement of Federated learning and it also doesn't share the data between devices.
We can use this learning method to train models on sensitive data.
Talking about security, Fusion Learning wraps up the privacy-preserving characteristics of Federated Learning. It never shares the raw data with other devices. Additionally, knowledge distillation method is used to protect the privacy of local models which are device models by compressing the information before sending it to the server central model.
Application of Fusion Learning
There are various application of fusion learning, here are some of the applications of fusion learning −
In Image classification − We use the fusion learning to improve the accuracy of image classification models. We combine the outputs of multiple models into single one which becomes useful for tasks where there is a lot of variation in the data, for example classifying images of animals or plants.
Natural language processing − Fusion learning is also used to improve the accuracy of natural language processing (NLP) models. What it does is it combines the outputs of multiple models into single one like we do in image classification. Fusion learning is very useful for tasks where there is a lot of ambiguity in the data for example sentiment analysis or machine translation.
Internet of Things (IoT) − We use the Fusion learning concept to train the models locally and share the output result knowledge for building better global models while preserving user privacy.
Healthcare − We use Fusion learning for improving the accuracy of models which are used for the medical diagnosis. It first predicts the output of the model then it combines the output of the multiple model. This is very useful for tasks where there is a lot of variation in the data, for example the model which is used to diagnose cancer or heart disease.
Robotics − Fusion learning can be used to improve the accuracy of robotic models by combining the outputs of multiple sensors. This is especially useful for tasks where there is a lot of uncertainty in the environment, for example navigating through a cluttered room.
Finance − We can use the Fusion learning in the Financial institutions for enhancing fraud detection models without compromising customer data.
So, we learned about Fusion Learning which is very powerful technique and it is used to improve the accuracy of a variety of machine learning tasks. Fusion Learning, also known as One Shot Federated Learning and talking about security aspects of training the model then it is used widely as it does not share the raw data of devices with any other devices and it also decreases the multiple round communication to one round which improves model performance. As technology will grow we can expect it to play crucial role in the terms of privacy for various machine learning applications. You can also use the concept of this model and apply it to your ML model.
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