Federated learning (FL) is a promising distributed learning approach to enable intelligent Internet of Things (IoT) applications. However, FL is vulnerable to model poisoning attacks in which malicious clients abate the accuracy of the global model by committing crafted local model updates to the server. Existing defense methods either rely on a validation data set or simply remove the detected malicious clients from the subsequent training process to handle attacks from a large number of malicious clients. Thus, the performance of existing methods deteriorates drastically in many scenarios where the data distributions of clients are unpredictable. To address these deficiencies, we propose a framework called FL overwatch (FLOW) to efficiently defend against model poisoning attacks taking advantages of the local model updates in current and historical training iterations. On one hand, FLOW detects malicious clients in each iteration by measuring the cosine distances between the local model updates of clients, such that malicious updates are eliminated from the current aggregation. On the other hand, FLOW gracefully punishes the previously identified malicious clients rather than removes them from the whole training process. As a result, FLOWcan embrace a richer reliable set of local model updates than existing methods in aggregation. Extensive experiments on widely used benchmark data sets show that FLOW can achieve higher success defending ratio and higher accuracy of global models over existing Byzantinerobust FL methods under typical untargeted attacks and targeted attacks. Furthermore, FLOW also shows significant effectiveness in defending against adaptive attacks tailored to FLOW.

To cope with the complex, diverse and rapidly changing battlefield environment sensing needs, a mission-oriented adaptive management system for multi-sensors network in the network centric warfare is proposed. Based on the principle of openness, adaptivity and robustness, the mission decomposition model, Meta-task sequence diagram construct method, sensor management plan optimization, and strategic guiding are designed to generate the mission-oriented sensor management plans. By virtue of the event response mechanism, the system responses quickly to emergency battlefield situations and control from the command system. It forms a feedback loop structure to improve the effectiveness of the mission-oriented multi-sensor network management.

The commonly-used large-scale knowledge bases have been facing challenges in open domain question answering tasks which are caused by the loose knowledge association and weak structural logic of triplet-based knowledge. To find a way out of this dilemma, this work proposes a novel metaknowledge enhanced approach for open domain question answering. We design an automatic approach to extract metaknowledge and build a metaknowledge network from Wiki documents. For the purpose of representing the directional weighted graph with hierarchical and semantic features, we present an original graph encoder GE4MK to model the metaknowledge network. Then, a metaknowledge enhanced graph reasoning model MEGr-Net is proposed for question answering, which aggregates both relational and neighboring interactions comparing with R-GCN and GAT. Experiments have proved the improvement of metaknowledge over main-stream triplet-based knowledge. We have found that the graph reasoning models and pre-trained language models also have influences on the metaknowledge enhanced question answering approaches.