Artificial intelligence (AI) is a reality and has woven its way into everyday life: navigation systems, spam filters, weather forecasts to name but a few.
There have been significant advancements in the field of machine learning, a subset of AI. The machines learn due to the complex algorithms that allow them to analyse huge data sets and make predictions using these data. Along with enhancing the machines’ skills, increasing amounts of data are being collected and information on human behaviour are being monitored; all of which present challenges for privacy and data protection.
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The 5th G7 Data Protection and Privacy Authorities Roundtable took place on 18 to 19 June in Ottawa, Canada.
Following you can find the documents adopted during this event:
Federated Learning (FL) presents a promising approach to machine learning (ML) by allowing multiple sources of data (devices or entities) to collaboratively train a shared model while keeping data decentralised. This approach mitigates privacy risks as raw data remains locally on the sources, which is particularly beneficial in scenarios where data sensitivity or regulatory requirements make data centralisation[1] impractical. Applications of FL are diverse, spanning personalised recommendations, healthcare data analysis, data spaces, and autonomous transport systems, where ensuring privacy and data protection is paramount.
Speech by the EDPS Secretary General, Leonardo Cervera Navas, at CPDP - Data Protection Day.