Federated learning for performance prediction in multi-operator environments

Telecom vendors and operators deliver services with strict requirements on performance, over complex and sometimes partly shared network infrastructures. A key enabler for network and service management in such environments is knowledge sharing, and the use of data-driven models for performance prediction, forecasting, and troubleshooting. In this paper, we outline a multi-operator service metrics prediction framework using federated learning that allows privacy-preserved knowledge-sharing across operators for improved model performance, and also reduced requirements on data transfer within an operator network. Federated learning is compared against local and central learning strategies for multi-operator performance prediction, and it is shown to balance the requirements on data privacy, model performance, and the network overhead. Further, the paper provides insights on how data heterogeneity affects model performance, where the conclusion is that standard federated learning has certain robustness to data heterogeneity. Finally, we discuss the challenges related to training a federated learning model with a limited budget on the communication rounds. The evaluation is performed using a set of realistic publicly available data traces, that are adapted specifically for the purpose of studying multi-operator service performance prediction

Impact of 4D-Flow CMR Parameters on Functional Evaluation of Fontan Circulation

We sought to evaluate the potential clinical role of 4D-flow cardiac magnetic resonance (CMR)-derived energetics and flow parameters in a cohort of patients’ post-Fontan palliation. In patients with Fontan circulation who underwent 4D-Flow CMR, streamlines distribution was evaluated, as well a 4D-flow CMR-derived energetics parameters as kinetic energy (KE) and energy loss (EL) normalized by volume. EL/KE index as a marker of flow efficiency was also calculated. Cardiopulmonary exercise test (CPET) was also performed in a subgroup of patients. The population study included 55 patients (mean age 22 ± 11 years). The analysis of the streamlines revealed a preferential distribution of the right superior vena cava flow for the right pulmonary artery (62.5 ± 35.4%) and a mild preferential flow for the left pulmonary artery (52.3 ± 40.6%) of the inferior vena cave-pulmonary arteries (IVC-PA) conduit. Patients with heart failure (HF) presented lower IVC/PA-conduit flow (0.75 ± 0.5 vs 1.3 ± 0.5 l/min/m2, p = 0.004) and a higher mean flow-jet angle of the IVC-PA conduit (39.2 ± 22.8 vs 15.2 ± 8.9, p < 0.001) than the remaining patients. EL/KE index correlates inversely with VO2/kg/min: R: − 0.45, p = 0.01 peak, minute ventilation (VE) R: − 0.466, p < 0.01, maximal voluntary ventilation: R:0.44, p = 0.001 and positively with the physiological dead space to the tidal volume ratio (VD/VT) peak: R: 0.58, p < 0.01. From our data, lower blood flow in IVC/PA conduit and eccentric flow was associated with HF whereas higher EL/KE index was associated with reduced functional capacity and impaired lung function. Larger studies are needed to confirm our results and to further improve the prognostic role of the 4D-Flow CMR in this challenging population

Omenanlehtikirppu (Psylla mali schmidb.)

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Kahukärpänen ( Oscinella frit L.)

vokKirjasto Aj-

Kääpiöohrakärpänen (Chlorops pumilionis BJERK.)

vokKirjasto Aj-