Federated learning : an alternative approach to improving medical data privacy and security

While medical data is integral to building robust predictive machine learning models for medical research, obtaining access to medical data is increasingly difficult. The challenges primarily arise from obtaining consent, concerns around the privacy and security of medical data and the technical challenge of migrating what can be huge datasets to a centralised location. As a result, this chapter analyses the question, "How can we make medical data more accessible for medical research whilst addressing the ethical and technical issues around data privacy and data-sharing?" Moreover, this work expands on federated learning that represents a paradigm shift in machine learning from both a technical and sociological perspective. From a technical perspective, federated learning enables machine learning models to be trained in a decentralised manner. It thus allows researchers to utilise data stored in separate locations. From a sociological perspective, federated learning represents a shift in the power dynamic between those providing and those using medical data for research. Under a federated learning framework, raw data never leaves the client's device. Instead, the centralised server only receives encrypted parameter updates after a shared model is sent and trained locally on each client device. This ensures that the entities that provide medical data have more control over where their data is stored and what information is shared with other parties. Even though federated algorithms have a slightly lower accuracy when compared to non-federated algorithms, it comes with data privacy benefits that non-federated algorithms cannot provide.