Federated machine learning The road to decentralised data collaboration

Big data projects and state-of-the-art data science models are using artificial intelligence (AI) and machine learning (ML) to drive innovation across financial services, healthcare, government and other sectors. Take the healthcare industry for example, which is expected to spend roughly $23 billion globally on big data analytics by 2023, according to P&S Intelligence. Medical and life sciences organisations are embarking on AI and ML initiatives to unlock complex data sets with the goal of preventing diseases, speeding recovery and improving patient outcomes. And, financial services institutions are using these systems to bolster fraud detection efficacy, and federal governments are applying it for public data sharing to support R&D and improved public services (and the list goes on and on).The sensitive nature of the data used in deep learning projects  including data ownership issues and regulatory requirements such as the General Data Protection Regulation (GDPR), HIPAA, financial data privacy rules, etc. require organisations to go to great lengths to keep information private and secure. As a result, data sets that could be tremendously valuable in concert with other initiatives (or organisations) are often locked away and guarded, creating data silos. But as a variety of industries begin to spread their wings with AI and ML technology, we’re seeing a groundswell of overwhelming demand for innovative, trusted and inclusive solutions to the data collaboration problem. Organisations are asking for a way to execute deep learning algorithms on data sets from multiple parties, while ensuring that the data source is not shared or compromised, and that only the results are shared with approved parties.