Autonomous systems generate a huge amount of multimodal data that are collected and processed on the Edge, in order to enable AI-based services. The collected datasets are pre-processed in order to extract informative attributes, called features, which are used to feed AI algorithms. Due to the limited computational and communication resources of some CPS, like autonomous vehicles, selecting the subset of relevant features from a dataset is of the utmost importance, in order to improve the result achieved by learning methods and to reduce computation and communication costs. Precisely, feature selection is the candidate approach, which assumes that data contain a certain number of redundant or irrelevant attributes that can be eliminated. The quality of our methods is confirmed by the promising results achieved on two different data sets. In this work, we propose, for the first time, a federated feature selection method suitable for being executed in a distributed manner. Precisely, our results show that a fleet of autonomous vehicles finds a consensus on the optimal set of features that they exploit to reduce data transmission up to 99% with negligible information loss.
翻译:自动系统生成大量多式数据,这些数据在边缘收集和处理,以便能够提供AI服务。所收集的数据集是预先处理的,以便提取信息属性,称为功能,用来提供AI算法。由于一些CPS的计算和通信资源有限,像自治车辆一样,从数据集中选择相关特性的子集至关重要,以便改进学习方法取得的成果,降低计算和通信成本。准确地说,特征选择是候选方法,它假定数据含有一定数量的多余或无关的属性,可以消除。两种不同的数据集所取得的有希望的结果证实了我们的方法的质量。在这项工作中,我们首次建议采用适合以分布方式执行的节点特征选择方法。确切地说,我们的结果表明,一个自主车辆车队就最佳特征集达成共识,利用这些特征集来减少99%的数据传输,造成微不足道的信息损失。