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5G networks with the vast number of devices pose security threats. Manual analysis of such extensive security data is complex. Dark-NMF can detect malware activities by monitoring unused IP address space, i.e., the darknet. However, the challenges of cooperative training for Dark-NMF are immense computational complexity with Big Data, communication overhead, and privacy concern with darknet sensor IP addresses. Darknet sensors can observe multivariate time series of packets from the same hosts, represented as intersecting columns in different data matrices. Previous works do not consider intersecting columns, losing a host's semantics because they do not aggregate the host's time series. To solve these problems, we proposed a federated IoT malware detection NMF for intersecting source hosts (FINISH) algorithm for offloading computing tasks to 5G multiaccess edge computing (MEC). The …