Federated Learning is a machine learning approach that allows models to be trained across numerous devices or servers holding local data samples, without exchanging them. This approach is particularly useful in the era of cloud computing, where data privacy and security are paramount. Federated Learning ensures that all the training data remains on the original device, enhancing privacy and efficiency.
Cloud computing, on the other hand, is the delivery of different services through the Internet. These resources include tools and applications like data storage, servers, databases, networking, and software. Rather than keeping files on a proprietary hard drive or local storage device, cloud-based storage makes it possible to save them to a remote database. As long as an electronic device has access to the web, it has access to the data and the software programs to run it.
Definition of Federated Learning
Federated Learning is a decentralized learning approach that allows a model to learn from many clients' data while keeping all the data on the original device. This approach is a solution to the challenges of data privacy, data security, data access, and data volume in machine learning. The central server does not need to see the raw data to train the model; instead, it learns from a summary of updates from each client's locally trained model.
This method is particularly useful in situations where data cannot be shared due to privacy concerns or regulations. For example, in healthcare, patient data cannot be moved due to privacy laws. Federated Learning allows a model to be trained on this data without moving it, thereby complying with the laws.
Definition of Cloud Computing
Cloud computing is the on-demand availability of computer system resources, especially data storage and computing power, without direct active management by the user. The term is generally used to describe data centers available to many users over the Internet. It is named cloud computing because the information being accessed is found remotely in the cloud or a virtual space. Companies that provide cloud services enable users to store files and applications on remote servers, and then access all the data via the Internet.
Cloud computing provides a simple way to access servers, storage, databases and a broad set of application services over the Internet. A cloud services platform such as Amazon Web Services owns and maintains the network-connected hardware required for these application services, while you provision and use what you need via a web application.
History of Federated Learning
Federated Learning, as a concept, was first introduced by Google in 2016. The primary motivation behind this approach was to improve the privacy and security of machine learning while also improving the efficiency of the learning process. Google used this approach to train the predictive model of its keyboard, Gboard, on users' devices. This allowed the model to learn from the data without it ever leaving the device, thereby preserving the privacy of the users.
Since then, Federated Learning has been adopted by various industries, especially those dealing with sensitive data. For example, in healthcare, Federated Learning is used to train models on patient data without moving it. This not only preserves the privacy of the patients but also allows the model to learn from a diverse set of data, thereby improving its performance.
History of Cloud Computing
The concept of cloud computing dates back to the 1960s, when John McCarthy opined that "computation may someday be organized as a public utility." The actual term "cloud" borrows from telephony in that telecommunications companies, who until the 1990s primarily offered dedicated point-to-point data circuits, began offering Virtual Private Network (VPN) services with comparable quality of service but at a much lower cost.
Cloud computing has been credited with increasing competitiveness through cost reduction, greater flexibility, elasticity and optimal resource utilization. It has a potential for business to avoid upfront infrastructure costs, and focus on projects that differentiate their businesses instead of on infrastructure.
Use Cases of Federated Learning
Federated Learning has a wide range of use cases, especially in industries dealing with sensitive data. In healthcare, Federated Learning can be used to train models on patient data without moving it, thereby preserving the privacy of the patients. In finance, Federated Learning can be used to detect fraudulent transactions without sharing sensitive financial data. In telecommunications, Federated Learning can be used to improve network performance without sharing user data.
Another use case of Federated Learning is in the field of Internet of Things (IoT). With the increasing number of IoT devices, the amount of data generated is enormous. Federated Learning allows models to be trained on this data without moving it, thereby reducing the bandwidth requirements and improving the efficiency of the learning process.
Use Cases of Cloud Computing
Cloud computing is used for a variety of services, including email, virtual desktops, software development and testing, and data analysis. It also provides services such as data storage, backup and recovery, and data analytics. Companies can use cloud computing to run their applications and services, store and analyze their data, and leverage powerful computing resources on demand.
Cloud computing is also used in the development of new applications. It provides a scalable and cost-effective platform for developing and testing new applications, and for hosting applications that can be accessed by users worldwide. This makes it an ideal solution for businesses of all sizes, from startups to large enterprises.
Examples of Federated Learning
One of the most notable examples of Federated Learning is Google's Gboard. Google uses Federated Learning to train the predictive model of Gboard on users' devices. This allows the model to learn from the data without it ever leaving the device, thereby preserving the privacy of the users.
Another example of Federated Learning is in the healthcare industry. Companies like Owkin use Federated Learning to train models on patient data without moving it. This not only preserves the privacy of the patients but also allows the model to learn from a diverse set of data, thereby improving its performance.
Examples of Cloud Computing
Amazon Web Services (AWS) is a comprehensive, evolving cloud computing platform provided by Amazon that includes a mixture of infrastructure as a service (IaaS), platform as a service (PaaS) and packaged software as a service (SaaS) offerings. AWS services can offer an organization tools such as compute power, database storage and content delivery services.
Another example of cloud computing is Microsoft Azure, a cloud computing service created by Microsoft for building, testing, deploying, and managing applications and services through Microsoft-managed data centers. It provides software as a service (SaaS), platform as a service (PaaS) and infrastructure as a service (IaaS) and supports many different programming languages, tools and frameworks, including both Microsoft-specific and third-party software and systems.