The Case for the G7 to Adopt Federated Learning

Artificial intelligence (AI) is reshaping sectors like healthcare and banking, but it poses privacy challenges, prompting G7 leaders to explore solutions. As they convene in Alberta, the focus is on developing AI systems that respect privacy without hindering innovation. Federated learning (FL) emerges as a key, often overlooked, strategy. Unlike traditional AI, which centralizes data, FL trains models locally, sharing only updates, thus mitigating data breach risks. This method has successfully been applied in Canada for cancer detection and chronic disease prediction without exposing sensitive information. As governments rush to regulate AI, FL could facilitate cross-jurisdictional collaboration on critical issues while safeguarding local data control. For democracies, endorsing FL not only signals a commitment to ethical governance but also promotes trust in AI. The G7 should prioritize FL in its discussions to foster a secure, equitable AI future, leveraging existing technologies to create a foundation for privacy and innovation.

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