Federated Learning and Privacy

Definitions of the terms and much more...  

Federated Learning and Privacy

By Kallista Bonawitz, Peter Kairouz, Brendan Mcmahan, Daniel Ramage

Communications of the ACM, April 2022, Vol. 65 No. 4, Pages 90-97  10.1145/3500240

Machine learning and data science are key tools in science, public policy, and the design of products and services thanks to the increasing affordability of collecting, storing, and processing large quantities of data. But centralized collection can expose individuals to privacy risks and organizations to legal risks if data is not properly managed. Starting with early work in 2016,13,15 an expanding community of researchers has explored how data ownership and provenance can be made first-class concepts in systems for learning and analytics in areas now known as federated learning (FL) and federated analytics (FA).

With this expanding community, interest has broadened from the initial work on federations of mobile devices to include FL across organizational silos, Internet of Things (IoT) devices, and more. In light of this, Kairouz et al.10 proposed a broader definition:

Federated learning is a machine learning setting where multiple entities (clients) collaborate in solving a machine learning problem, under the coordination of a central server or service provider. Each client's raw data is stored locally and not exchanged or transferred; instead, focused updates intended for immediate aggregation are used to achieve the learning objective.

An approach very similar in both philosophy and implementation, federated analytics17 can be taken to allow data scientists to generate analytical insight from the combined information in decentralized datasets. While the focus here is on FL, much of the discussion on technology and privacy applies equally well to FA use cases.

This article provides a brief introduction to key concepts in federated learning and analytics with an emphasis on how privacy technologies may be combined in real-world systems and how their use charts a path toward societal benefit from aggregate statistics in new domains and with minimized risk to individuals and to the organizations who are custodians of the data.  ...... ' 

Thinking Federated AI

Good general idea,  further detailed at the link.

Changing How Data is Used  By Samuel Greengard,  Commissioned by CACM Staff, June 1, 2021

The ability to examine data in deep and revealing ways has fundamentally changed the world. Yet, despite enormous advances in analytics and machine learning, researchers, businesses, and governments remain hamstrung by a basic but nagging problem: a lot of data is sensitive and sharing it presents security and privacy risks.

Although numerous technologies, such as data tokenization, k-anonymity, homomorphic encryption and Trusted Execution Environments (TEE) have helped tame the beast, they do not address the fundamental problem that sensitive data is crossing undesired boundaries. Consequently, many organizations hesitate to share data that could yield remarkable insights. The problem is especially acute when organizations have trade secrets, customer data, or personally identifiable information (PII) embedded in data.

An emerging framework called Federated AI (artificial intelligence) Learning could change all of this, while ushering in broader changes in the way data is used, and owned. The technology has major repercussions for machine learning, security, and privacy. "It represents the future of secure machine learning," says Eliano Marques, vice president of data and AI at data security firm Protegrity.

Rethinking the Model

Federated AI takes the conventional idea of machine learning and turns it on its head. Instead of multiple groups sending data to a central cloud where the machine learning takes place, the algorithm travels to the computing device. All training is performed on the client or device, and when the algorithm determines that it is finished, it exits the device, taking the results with it. "There is no data sharing," says Jigar Mody, head of AI services at Oracle.  ... '