在FL系统中,没有关于每个参与者的数据集中的数据分布的假设,也没有关于每个分布式数据集的大小的假设。 这种灵活性是该技术的另一个关键优势。 它允许具有不同数据量或数据分布和不同功能的不同参与者在ML模型的训练上进行协作。 具有异构数据集可能有助于构建可以更好地概括的模型。 然后,每个参与者都可以受益于在更丰富和更广泛的数据范围上训练的模型,而无需花费额外的成本来获取此数据。
联合学习应用(Federated learning applications)
Federated learnning, with its many advantages, has applications in many industries:
联合学习具有许多优点,已在许多行业中得到应用:
A canonical example is Google’sGboard, where FL has been used to train the predictive keyboard algorithm directly on millions of user’s smartphones, without needing to upload any of the user’s interactions or private text messages in the cloud. Instead, neural network models were sent to the user’s devices, trained locally on data stored in the devices, and sent back with additional privacy preserving methods to be averaged with thousands of other devices’ updates. Despite the stringent technical constraints of having to train models directly on devices without affecting the user experience, the obligation to preserve users’ privacy provided a compelling use case for FL. For the user, the improved and personalised experience using the keyboard provides an incentive for accepting to participate in the model training.