{'fraction_non_alphanumeric': 0.014412416851441241, 'fraction_numerical': 0.0011086474501108647, 'mean_word_length': 5.837121212121212, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Missing Data Imputation for Multivariate Time series in Industrial IoT: A Federated Learning Approach

In multidimensional times series generated by sensor recordings of multiple dispersed IoT edge devices, missing measurements are commonplace due to sensing or communication failures, considered a thorny and perplexing problem in a wide range of Industry 4.0 applications. Existing studies for time series imputation focus on developing centralized deep learning approaches, which require massive amounts of data to be uploaded to a central server with adequate computational and power resources for the training of the models, since these approaches are unsuitable for edge and IoT devices characterized by limited computation resources. Different from the current literature, in this study, the time series imputation problem is studied from a federated learning perspective, which is able to surmount the above difficulties. In particular, a novel federated learning approach is proposed, assuming different IoT devices with varying sensing and computational capabilities, that trade-off accuracy with computational/communication/sensing complexity and minimize the operations that need to be performed during training and inferences phase. Furthermore, considering that the main computations are performed on the edge, where the IoT edge devices have limited computational capabilities and power resources, a lightweight yet effective autoencoder-based model is employed to address the examined problem, modified properly to capture the temporal dependencies of the time series data. Extensive evaluation studies with two open datasets have shown that both approaches minimize the data exchanges the need to be made for outperforming centralized approaches in the presence of limited training data.