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Towards a Proportionate and Risk-Based Approach to Federated Data Access in Canada
This CIFAR AI Insights Policy Brief explores how federated learning (FL) may be implemented. The authors discuss findings from document review, expert interviews, a validation workshop, and a survey of solutions to privacy, ethics and security challenges raised by FL. In evaluating solutions to potential challenges, they focus on a proportionate response to realized risks, specifically the frequency and magnitude of harm caused by ethical, privacy, and security breaches of health data. They discuss the trade-offs between protections and the utility of data for FL and recommend enabling governance models.
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