What is Federated Machine Learning (FML)?
Introduction to Machine Learning
Machine learning is one of the most exciting advancements in artificial intelligence. It allows computers to learn from data, without being explicitly programmed. Federated machine learning is a newer advancement in this field, that allows multiple organizations to share data securely. This has many benefits, including increased privacy and cost savings. In this blog post, we will explore what federated machine learning is and how it works.
Introduction to Federated Machine Learning
FML is a technique for training a machine learning model on data that is distributed across multiple devices. This can be done without sharing the raw data between devices. Instead, the models are trained on each device separately and then federated, or combined, to create a single model.
If you are new to federated learning be sure to check out Google's cartoon explaining everything you need to know.
What are the benefits to using Federated Machine Learning?
There are many benefits to training a model on an individual device and then combining it. One of the biggest benefits is increased privacy. When data is stored locally on each device, there is no need to share sensitive information with other organizations. This can be particularly important in fields like healthcare, where personal data must be protected.
Another benefit is the cost savings. Storing data locally at the edge reduces bandwidth costs and can also help companies decrease costs, especially as more and more data is being collected at the edge.
What is the difference between traditional Machine Learning and Federated Machine Learning?
The biggest difference is the way that data is stored and accessed. In traditional machine learning, data is typically stored in a central location, like a data warehouse or data lake. This can be expensive and time-consuming to set up and maintain. Federated machine learning allows organizations to store data locally on each device. This reduces costs and can also help to increase privacy.
What are some of the challenges with Federated Machine Learning?
FML has many potential benefits for organizations, including increased privacy and cost savings. However, there are also some challenges to consider, like synchronization issues and limited availability for certain data sets.
One of the biggest challenges with federated machine learning is that it can be difficult to keep models synchronized across devices. If one device is updated, the others need to be updated as well. This can be challenging to do in a timely manner. But BOSS AI makes this very easy using their Enterprise AI platform.
Another limitation of federated machine learning is data labels. In order to train a model, you need data that is labeled. This can be difficult to obtain if the data is distributed across different devices. With BOSS AI you get access to data transformations so you can cleanse your data before training.
What are some of the privacy implications of Federated Learning?
Federated learning can have a number of privacy implications, depending on how it is implemented.
If data is stored locally on devices, there is a risk that it could be leaked if the device is hacked or stolen.
There is also the risk that someone could reverse engineer the model to learn about the training data. This could be a concern if the data is sensitive.
Finally, federated learning could potentially allow companies to track users by their behavior. This would be a concern if you are using an app that uses federated learning.
Despite these risks, federated learning can be a great way to improve privacy and security while still allowing data to be shared across different organizations.
What is horizontal vs vertical Federated Machine Learning?
There is horizontal machine learning and vertical machine learning.
In horizontal machine learning, the data is distributed among different machines and it is used to train models.
In vertical machine learning, the data is distributed among different devices of the same type. For example, if you have a bunch of iPhones, you can use vertical learning to train models on all of the data from those iPhones.
What industries would benefit the most from Federated Machine Learning?
There are many industries that will benefit. Some of the most obvious benefits would be in fields like healthcare and finance, where privacy is a major concern. However, any industry that relies on data could potentially benefit from this technology.
FML is still a relatively new field and there are many potential applications that have yet to be explored. As more organizations begin to adopt this technology, we will likely see even more benefits emerge.
How does Federated Learning allow for personalization?
Federated learning can allow for personalization by allowing each device to learn from its own data. This can be particularly important in fields like healthcare, where each person's data is unique. By training models on each device separately, we can ensure that the models are personalized to each individual.
How would an organization get started using Federated Machine Learning?
If you're interested in using federated learning, there are a few things to keep in mind.
First, you'll need to choose what type of data you want to use. This will help determine what kind of federated learning is right for your organization.
Next, you'll need to decide what devices you want to use. You'll need to make sure that these devices are compatible with each other and can communicate with each other.
Finally, you'll need to set up a server that can manage the communication between the devices. This server will be responsible for training the models and distributing the updates. Start a free trial of BOSS AI today to make it easy to train your models across devices.
- Wikipedia, Wikimedia Foundation, 20 Apr. 2019