Faculty's privacy enhancing federated learning solution

预测传染病以预测暴发、季节性流行病和全球大流行的行为正在迅速成为现代公共卫生应对措施的重要组成部分。对感染风险进行建模是非常复杂的，并且受到一系列流行病学、社会经济和流动性因素的影响，获得大规模公共卫生数据对于准确地对疾病演变动态进行建模至关重要。然而，由于这些数据的隐私和由此产生的隐私法规的完全有效的敏感性，获取足够的数据进行建模可能是一个挑战。教职员工正在开发一种强大而安全的解决方案，以解决机器学习面临的最常见挑战之一，以实现隐私保护的联合学习。这一解决方案将使单个组织，从单个医院或初级保健实践，受益于共享的机器学习模型，该模型利用来自多个组织的大规模数据的好处，而不需要组织永远共享他们的数据。提供高性能机器学习的主要挑战之一是无法在足够大的规模、真实世界的数据集上促进模型训练，因为患者和组织通常有可以理解的隐私担忧。学院的联合学习解决方案将就个别组织本地持有的数据的私有化合成表示进行协作培训，从而避免GDPR或HIPAA的监管障碍，并确保足够大规模的数据。该解决方案不仅通过隐私保障加强数据隐私，而且授予每个贡献组织控制其隐私程度与绩效回报的权力，并设置独立于中央组织和其他贡献组织的控制，而无需任何原始或合成数据传输。

Forecasting infectious disease to predict the behaviour of outbreaks, seasonal epidemics and global pandemics is rapidly becoming an essential part of a modern public health response. Modelling infection risk is highly complex and influenced by a range of epidemiological, socioeconomic and mobility factors and access to large scale public health data is essential to accurately model evolving disease dynamics. However, due to the entirely valid sensitivities regarding the privacy of such data and the resulting privacy regulation, accessing sufficient data for modelling can present a challenge. Faculty are developing a powerful and safe solution to address one of the most common challenges facing machine learning to enable privacy preserving federated learning. This solution will empower individual organisations, as small as individual hospitals or primary care practices, to benefit from a shared machine learning model that harnesses the benefits of large scale data from multiple organisations without the need for organisations to ever share their data. One of the main challenges to delivering high performance machine learning is the inability to facilitate model training on sufficiently large scale, real world, datasets as patients and organisations often have understandable privacy concerns. Faculty's federated learning solution will collaboratively train on privatised synthetic representations of individual organisations locally held data, thus avoiding GDPR or HIPAA regulatory hurdles, and securing sufficiently large scale data. This solution not only strengthens data privacy through privacy guarantee, but grants each contributing organisation the power to control the extent of their privacy vs performance pay offs and set the controls independently of both the central organisation and of each other contributing organisation without any raw or synthetic data transfer.