Reading list

A brief introduction to federal learning

Picture: Lianhao Qu on Unsplash

Our digital footprint means anything we write, watch, read, send, listen to and buy The Internet is a goldmine for an industry that wants to track and collect information. A recent episode of the TDS Podcast explored privacy in modern artificial intelligence technology and how security practices are constantly lacking. Jeremie Harris, podcast host, spoke with Eliano Marques, vice president of data and artificial intelligence at Protegrity. His insights ranged from private business initiatives to targeted ads for privacy awareness, and he made excellent comments on teaching designed to teach the public what all this means. Return to the section here:

An incredibly important but misunderstood concept, artificial intelligence privacy is a topic we should all strive to learn more about, so I dug around the TDS for articles that expand on this topic. I ended up in the information rabbit hole federal learning, and found that it related quite well to the topics covered in the podcast episode. This list serves as a suitable resource for beginners to learn data collection strategies.

Artificial intelligence difference and combined learning

In the newsletter from July 2019, Pier Paolo Ippolito introduced the Federated Learning concept: “Technology for training machine learning models from data to which we do not have access”, which, as the article states, is a critical measure in data protection measures. This serves as an excellent presentation resource to learn about the implications of data collection for both individuals and data collectors.

Decentralization of Artificial Intelligence and Protection of Privacy: The Genius of Federation Learning

Andre Ye published this informative, easy-to-understand chapter on the benefits of ethical and non-invasive machine learning in September 2020. You did an excellent job of illuminating federated learning in a pleasant context – which is quite rare and difficult to nail in scientific and technical writing. He asks a key question like this: “How can the benefits of big data for more individual and engaging experiences be realized while defending – not just acknowledging – the privacy of users’ information?” The answer, combined learning, drives past the arguments for and against data collection to imagine the middle ground: The best-case scenario for the inevitable part of life.

A strategy for artificial intelligence in an era of vertical blended learning and knowledge sharing

Alexandre GonfalonieriPhillips ’artificial intelligence consultant wrote about sharing information in large-scale machine learning projects in May 2020. He found that blended learning depended on the use case and model type, the reward system adopted by other“ participants ”. the data to be shared, the number of companies involved in data sharing, the neutral FL coordinator selected, and the cost of local training and online communications. Gonfalonieri brought the full circle of the subject by applying this theory to smart retail, finance, and healthcare as well.


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