Federated and swarm learning are distributed learning frameworks for machine learning training sets. These frameworks enable AI models to be trained on distributed datasets across organizational silos or across disparate organizations to provide more comprehensive datasets versus localized and siloed datasets. This helps to provide more robust AI models and address bias inherent in localized healthcare datasets.
Federated and Swarm Learning in Healthcare
The Analyst Team
Andrew Brosnan
Principal Analyst, AI Applications in Life Sciences
Andrew is a principal analyst in Omdia’s AI & Intelligent Automation practice, with a vertical focus of AI use cases and AI business impact, primarily in healthcare and financial services.
Based in Oxford, his career of 30 years spans the professional and financial services and life sciences sectors as an IT practitioner, industry analyst, and consultant. Andrew has worked in IT for multinational organizations such as JP Morgan and Merck Pharmaceuticals and as an industry analyst and market researcher for Grant Thornton and Goldman Sachs in corporate finance and investment banking, respectively. As an industry analyst, Andrew has advised enterprises on digital transformation to improve operational efficiency, IT product selection, business case development, best practice, IT strategy, and implementation. Andrew advises tech companies on product strategy and development, competitive intelligence, use cases, corporate development strategy, and go-to-market messaging. Andrew holds a bachelor’s degree in economics from Boston University and an MBA from the University of Warwick.
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