Building privacy into AI: is the future federated?

This article from Aydin Ulas, Data Scientist at Trūata recently featured in Infosecurity Magazine.

The changing dynamics of the digital world have led to several privacy challenges for businesses, large and small. This is placing increasing pressure on them to evolve their processes and strategies. Much of the burden stems from the sheer volume of data present today, and in fact, the volume of data is predicted to balloon to 175 zettabytes (ZB) by 2025. Today, it is simply beyond human capability to effectively process and protect privacy without the assistance of privacy-enhancing technologies (PETs).

This has led to an explosion of adaptive machine learning (ML) algorithms that can wade through the mountain of data while continuously and efficiently changing their behavior in real-time as new data streams are fed into them. However, while ML is key to leveraging and learning from big data at scale, it can create privacy challenges. In fact, traditional ML requires data to be stored on a centralized server for analysis, including transporting data to cloud environments; this opens the doors to a plethora of security and privacy implications.

Taking it to the Edge

These privacy and security concerns have led the charge for ML technology that can work in a way that preserves consumer privacy, which is why federated learning (FL) has gained such momentum. Federated learning, put simply, is a decentralized form of machine learning. It is a method of training an algorithm on user data across multiple decentralized edge devices or servers without exchanging or transferring that data to a central location.

With decentralized, federated learning, a global model is generated in a central server, and the data to train this model is distributed across edge devices. The data stays with the owner while still being used to create insights centrally. If you will, bringing the mountain to Muhammad, the model is brought to the data where it can be trained/updated rather than the data having to go to the model. Federated learning is one of the best examples of the new breed of edge computing, where computation and data storage are brought closer to the data source.

Looking to support a privacy-first future for web advertising and protect its biggest revenue stream, Google is a leading proponent of the technology and has recently launched its Federated Learning of Cohorts (FLoC) as a replacement for traditional third-party cookies, which it plans to stop supporting by 2023.

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