事实证明,一些简单而直观的FL方法是实际应用中令人惊讶的高效解决方案,其中一个示例就是联合平均算法。 它包括定期对由不同FL参与者(称为工作者)训练的神经网络的权重进行平均,以权衡其本地数据子集以更新全局模型。 反过来,然后使用此新的全局模型更新局部神经网络以进行进一步的训练。 随着全局模型的更新,从每个本地数据集获得的学习信息将逐步在所有工作人员之间共享。 这是我们在农业应用中应用于图像分类用例的方法,下面将详细介绍。
See Figure 1 below a for an illustration of the federated learning principles in this case.
有关这种情况下的联合学习原理的说明,请参见下面的图1。
是什么使联合学习如此吸引人?(What makes federated learning such an appealing technique?)
Machine learning applications require vast amounts of data. Acquiring sufficient quantities of data to solve a specific problem with ML can be challenging, time-consuming and costly. In practical cases, the data generated is often not centralised, but dispersed instead, and needs to be gathered first from many sources for being used. In addition, the data acquired by an organisation can have a specific distribution that may not support the development of models that can generalise well. For example, hospitals in different regions or countries may have different distributions of patients profiles and pathologies, and would benefit from collaborating to develop ML applications that serve equally all their patients. While those challenges could be theoretically addressed through the collaborations of organisations sharing a common problem and interest, such a collaboration is in practice a very hard problem to solve in any context, when it comes to exchanging data and information.
机器学习应用程序需要大量数据。 获取足够数量的数据来解决ML的特定问题可能是具有挑战性,耗时且昂贵的。 在实际情况下,生成的数据通常不是集中的,而是分散的,需要首先从许多来源收集以便使用。 此外,组织获取的数据可能具有特定的分布,可能无法支持可以很好地推广的模型的开发。 例如,不同地区或国家/地区的医院可能具有不同的患者资料和病理分布,并且将受益于合作开发可同时为所有患者提供服务的ML应用程序。 尽管理论上可以通过拥有共同问题和共同利益的组织的协作来解决这些挑战,但在交换数据和信息时,在任何情况下,这样的协作实际上都是一个很难解决的问题。
The federated learning approach enables the collaborative development of more robust and performant machine learning models, while addressing critical issues such as data transfer, privacy, and security for each individual participant.
联合学习方法可以协作开发更强大和性能更好的机器学习模型,同时为每个参与者解决诸如数据传输,隐私和安全性等关键问题。
Because in a FL system the data of each participant is not transferred and remains under the participant’s control, it provides a solution to the problem of privacy preservation when considering working in collaboration. This privacy-by-design characteristic is one of the main advantages of FL. Data privacy matters whether different organisations consider collaborating on solving a common set of problems, or when protecting sensitive and personal data is crucial but using this data would also be beneficial, such as in the case of health data.With FL, the security and access to the data can be managed locally, under the participant’s security and privacy requirements. The potential is huge, as privacy and ownership concerns are one of the main barriers to data sharing.