Addressing AI Security Threats: The Case for the G7 to Adopt ‘Federated Learning’ to Enhance SEO.
Artificial Intelligence (AI) is revolutionising various sectors, from diagnosing diseases in hospitals to detecting fraud in banking systems. However, this transformation raises critical questions about privacy. As G7 leaders convene in Alberta, a significant issue emerges: how can powerful AI systems be developed without compromising individual privacy? The G7 summit presents an opportunity for democratic nations to establish guidelines for managing emerging technologies. While regulatory frameworks are evolving, their effectiveness hinges on robust technical solutions. Federated Learning (FL) stands out as a promising yet often overlooked tool that should be central to this discussion. Researchers in AI, cybersecurity, and public health have witnessed the data dilemma, where AI relies heavily on personal data, such as medical histories and financial transactions. Centralised data systems pose significant risks, including data breaches and misuse, as evidenced by the United Kingdom’s National Health Service pausing an AI initiative due to data handling concerns.
The growing liability of centralised AI systems stems from their vulnerability to cyberattacks and regulatory challenges, particularly when data crosses national boundaries. Centralised systems concentrate power in the hands of a few tech giants, making them attractive targets for hackers. In contrast, FL offers a solution by bringing the algorithm to the data rather than centralising the data itself. Local institutions, such as hospitals and banks, can train AI models on their own data, sharing only model updates with a central system. This method significantly reduces the risk of data breaches while still allowing for the analysis of large-scale trends. FL, when combined with techniques like differential privacy and secure multiparty computation, has the potential to transform data handling. In Canada, researchers have successfully employed FL to develop cancer detection models across provinces without transferring sensitive health records, showcasing its effectiveness and promise.