Why is federated learning so crucial for AI?

Federated Learning (FL) is a machine learning method in which a model is trained on multiple devices, such as smartphones or edge devices, rather than a centralized server. The devices, also known as clients, train a model on their own data and then send updates to a central server. After that, the server aggregates the updates and returns the improved model to the clients. This procedure is repeated until the model achieves the desired level of precision.One of the main advantages of FL is that it allows for training models on a large amount of data without the need to send all of the data to a central location. This can be especially useful for sensitive data that cannot be shared with a central server, such as personal information or medical data. Additionally, FL can also enable offline training, where a model is trained on a device that does not have a constant internet connection.In a nutshell, FL is a method of training a machine learning model that is distributed across multiple devices, where each device uses its own data to improve the model, and the model is improved by aggregating these updates from all the devices.‍

Research and science behind Federated Learning

Federated Learning (FL) is a relatively new field of study that has gained traction in recent years. It is based on the concept of distributed machine learning, which involves training a model on multiple devices rather than a central server. The research in FL is focused on addressing the challenges of training models in a distributed and decentralized environment, such as:

• Privacy and security: FL systems need to ensure that the data used to train the model remains private and secure. This can be achieved by using techniques such as differential privacy, homomorphic encryption, and secure multiparty computation.
• Communication efficiency: FL systems need to minimize the amount of data that needs to be sent between clients and the server. This can be achieved by using techniques such as compression, quantization, and model distillation.
• Model convergence: FL systems need to ensure that the model converges to a global optimal solution, despite the fact that the clients may have different and non-independent data distributions. This can be achieved by using techniques such as federated averaging, federated dropout, and federated momentum.
• Heterogeneity: FL systems need to handle the case where the clients have different data distributions, and/or different computational capabilities. This can be achieved by using techniques such as client selection, data augmentation, and adaptive learning rate.

In addition to these challenges, research in FL is also investigating new applications and use cases for FL, such as personalization, edge computing, and IoT (Internet of Things). Overall, FL is a multifaceted field with numerous research areas ranging from communication and security to optimization and machine learning. With the growing interest in FL, it is expected that research in this area will continue to grow and evolve.‍

Early adaptation on the bridge between research and the real world

Federated Learning (FL) is a relatively new technology, and it is still in the process of being adopted by the industry. First mover are digital technology companies, certain public institutions and Universities. However, there are already several examples of companies and organizations using FL to improve their products and services.

Here are a few examples of how FL is being used in the industry:

• Google: Google has been one of the pioneers in FL, using it to improve the performance of its keyboard app, Gboard. The app uses FL to train a model on the typing patterns of individual users, making predictions more accurate and reducing the amount of data that needs to be sent to a central server.
• Apple: Apple has also been exploring FL, using it to improve the performance of its Siri voice assistant. The company has been using FL to train a model on the speech patterns of individual users, making Siri more accurate and responsive.
• OpenAI: OpenAI has been working on FL to improve the performance of its GPT-3 model. The company has been using FL to train a model on the data of individual users, making the model more accurate and personalized.
• Alibaba: Alibaba has been using FL to improve the performance of its recommendation system. The company has been using FL to train a model on the browsing and purchasing habits of individual users, making recommendations more accurate and personalized.
• Meta: Facebook has been using FL to improve the performance of its text classification system. The company has been using FL to train a model on the text data of individual users, making the model more accurate and personalized.
• NASA / ESA: FL is used to interpret, classify, and search multiple satellite images for a variety of projects, the most prominent of which is the Earth Observation Project. The Technical University of Berlin leads a number of ESA research groups working on such platforms.

It is safe to say that FL is a rapidly evolving field that is expected to become more widely adopted in the industry in the coming years as businesses and organizations realize the benefits it can bring in terms of performance, privacy, and scalability. ‍

Commercial use of Federated Learning

There are several ways in which Federated Learning (FL) can be used in enterprise settings to improve business operations and decision making. Here are a few examples:

• Personalization: By training a model on the data of individual customers, an enterprise can personalize products and services to meet the specific needs of each customer. For example, a retail company could train a model on the browsing and purchasing habits of each customer and use the model to make personalized product recommendations.
• Predictive maintenance: By training a model on sensor data from equipment, an enterprise can predict when maintenance is needed and schedule it before a failure occurs. This can increase uptime and reduce costs.
• Fraud detection: By training a model on transaction data, an enterprise can detect fraudulent activity and take action to prevent it. This can reduce financial losses and improve customer trust.
• Image and video analysis: By training a model on image and video data, an enterprise can improve object detection and tracking, as well as facial recognition. This can be used in areas such as security, surveillance and self-driving cars.
• Edge computing: By training a model on data collected at the edge of a network, an enterprise can improve the performance and responsiveness of IoT devices, as well as reduce the amount of data that needs to be sent to a central server.

It’s important to note that FL can be used in conjunction with other machine learning techniques and technologies, such as cloud computing, big data platforms, and deep learning. As well as considering the technical aspects, it’s also important to take into account the organizational, legal and ethical issues related to using FL in an enterprise setting. Overall, FL has the potential to bring significant benefits to enterprises by allowing them to train models on data that is distributed across multiple devices, and providing a way to improve business operations and decision making in a privacy-preserving way.‍

Open Source projects for Federated Learning

There are several open-source Federated Learning (FL) projects that have been developed to make it easier for researchers and developers to experiment with FL and build their own FL systems. Here are a few examples:

• TensorFlow Federated (TFF): TFF is an open-source library for building FL systems using TensorFlow. It provides a set of APIs and tools for training models on federated data, as well as for implementing various FL algorithms.
• PySyft: PySyft is an open-source library for building FL systems using PyTorch. It provides a set of APIs and tools for training models on federated data, as well as for implementing various FL algorithms.
• OpenMined: OpenMined is an open-source community focused on developing tools and libraries for privacy-preserving machine learning, including FL. It provides a set of libraries and tools for building FL systems, as well as tutorials and resources for learning about FL.
• PaddleFL: PaddleFL is an open-source FL platform developed by Baidu which provides a set of tools and libraries for building FL systems, as well as a set of pre-built models and datasets.
• Apache Wayang (incubating): Apache Wayang is an open-source project that provides a set of libraries and tools for building FL systems. It aims to provide a common framework for FL developers, enabling them to create efficient, secure, and reliable FL systems.
• FL-Core: FL-Core is a lightweight, open-source, and easy-to-use framework for Federated Learning (FL) written in Python. It supports data parallelism and model parallelism, and makes it easy to build custom FL workflows.
• Leabra: Leabra is an open-source library for building neural networks and other machine learning models. It provides a set of tools and libraries for building FL systems, as well as a set of pre-built models and datasets.

These are just a few examples of open-source FL projects, and new ones are appearing regularly. These projects can be a great resource for researchers and developers who want to learn more about FL or build their own FL systems. ‍

What is the most advanced FL stack to use today?

It is difficult to say which Federated Learning (FL) stack is the most advanced and easy to use, as different stacks have different strengths and use cases. However, some of the most popular and widely used FL stacks are TensorFlow Federated (TFF), PySyft and Wayang.

• TensorFlow Federated (TFF) is an open-source library for building FL systems using TensorFlow. It provides a set of APIs and tools for training models on federated data, as well as for implementing various FL algorithms. TFF is widely used in industry and academia, and it has a large and active community of developers and users. TFF is integrated with TensorFlow and it is easy to use and understand for developers who are familiar with TensorFlow.
• PySyft is another open-source library for building FL systems using PyTorch. It provides a set of APIs and tools for training models on federated data, as well as for implementing various FL algorithms. PySyft is also widely used in industry and academia, and it has a large and active community of developers and users. PySyft is integrated with PyTorch and it is easy to use and understand for developers who are familiar with PyTorch.
• Apache Wayang (incubating) is a proposed open-source project under the Apache Software Foundation. It is a Federated Learning (FL) platform that provides a set of libraries and tools for building and deploying FL systems. The goal of Wayang is to provide an easy-to-use and extensible FL framework, allowing developers to create efficient and reliable FL systems with minimal effort. The Wayang platform supports data parallelism and model parallelism, as well as various types of communication between clients and servers. It is designed to be flexible, allowing developers to use different types of deep learning frameworks such as TensorFlow, PyTorch, etc. It also aims to provide a secure and privacy preserving solution by supporting secure multiparty computation, homomorphic encryption and differential privacy techniques.

The project is still in development and the features and capabilities may change as the project progresses. The Apache Wayang project is expected to be a valuable resource for developers looking to build FL systems, providing an easy-to-use and extensible framework for creating efficient, secure, and reliable FL systems. In general, TFF, PySyft and Wayang are considered to be easy to use, well-documented and have active communities. They are being used in various industries and have been tested in many projects. All have a wide range of functionalities, and they provide a good starting point for developers who want to experiment with FL. However, the best choice of FL stack will depend on the specific use case, and the experience of the developers.‍

The future of Federated Learning and decentralized AI training

The future outlook for Federated Learning (FL) is very promising, as it addresses several key challenges in the field of machine learning, such as data privacy, scalability and performance. One of the main areas of growth for FL is in the Internet of Things (IoT) and edge computing, where FL can be used to train models on data collected by IoT devices. This can improve the performance and responsiveness of the devices, as well as reduce the amount of data that needs to be sent to a central server.  Another area of growth for FL is in personalization, where FL can be used to train models on the data of individual users, making products and services more personalized and effective. FL is also expected to play a key role in the development of autonomous systems, such as self-driving cars, drones, and robots, where FL can be used to train models on the data collected by the systems, making them more accurate and reliable. In addition to these areas, FL is also expected to be used in a wide range of other industries and applications, such as healthcare, finance, and manufacturing. Overall, FL is a rapidly evolving field and it is expected to continue to grow in popularity and importance in the future, as more and more organizations and industries realize the benefits it can bring in terms of performance, privacy, and scalability. However, as with any new technology, there are also challenges to be overcome, such as ensuring data privacy, security, and compliance with regulations, as well as ensuring that the federated models are not biased or unfair. These challenges will need to be addressed in order to ensure that FL can reach its full potential.

About Databloom

Databloom.ai is a federated data access and analytics company that develops the federated analytics platform “Blossom Sky” to enable decentralized AI. It provides fast and interactive enterprise-ready distribution, consisting of additional tooling and configurations, enabling data scientists and analysts to run AI models and training against various decentralized data sources ranging in size from gigabytes to petabytes. Databloom is a leading contributor to Apache Wayang, the federated data processing engine. Want to know more? Get in touch with us via databloom.ai/contact.