Re-Weighted Softmax Cross-Entropy to Control Forgetting in Federated Learning

In Federated Learning, a global model is learned by aggregating model updates computed at a set of independent client nodes, to reduce communication costs multiple gradient steps are performed at each node prior to aggregation. A key challenge in this setting is data heterogeneity across clients resulting in differing local objectives which can lead clients to overly minimize their own local objective, diverging from the global solution. We demonstrate that individual client models experience a catastrophic forgetting with respect to data from other clients and propose an efficient approach that modifies the cross-entropy objective on a per-client basis by re-weighting the softmax logits prior to computing the loss. This approach shields classes outside a client's label set from abrupt representation change and we empirically demonstrate it can alleviate client forgetting and provide consistent improvements to standard federated learning algorithms. Our method is particularly beneficial under the most challenging federated learning settings where data heterogeneity is high and client participation in each round is low

Geometry of variational methods: dynamics of closed quantum systems

We present a systematic geometric framework to study closed quantum systems based on suitably chosen variational families. For the purpose of (A) real time evolution, (B) excitation spectra, (C) spectral functions and (D) imaginary time evolution, we show how the geometric approach highlights the necessity to distinguish between two classes of manifolds: K\"ahler and non-K\"ahler. Traditional variational methods typically require the variational family to be a K\"ahler manifold, where multiplication by the imaginary unit preserves the tangent spaces. This covers the vast majority of cases studied in the literature. However, recently proposed classes of generalized Gaussian states make it necessary to also include the non-K\"ahler case, which has already been encountered occasionally. We illustrate our approach in detail with a range of concrete examples where the geometric structures of the considered manifolds are particularly relevant. These go from Gaussian states and group theoretic coherent states to generalized Gaussian states.Comment: Submission to SciPost, 47+10 pages, 8 figure

Pulmonary complications for women with sickle cell disease in pregnancy: systematic review and meta-analysis.

BACKGROUND: Sickle cell disease (SCD) is a multisystem disease characterised by vaso-occlusive crisis, chronic anaemia and a shorter lifespan. More patients with SCD are living till reproductive age and contemplating pregnancy. Pulmonary complications in pregnancy are significant causes of maternal morbidity and mortality but yet this has not been systematically quantified. A systematic review and meta-analysis were conducted to quantify the association between SCD and pulmonary complications in pregnancy. METHODS: MEDLINE, EMBASE, Web of Science, Cochrane and Maternity and Infant Care databases were searched for publications between January 1998 and April 2019. Observational studies involving at least 30 participants were included. Random-effects models were used for statistical meta-analysis. FINDINGS: Twenty-two studies were included in the systematic review and 18 in the quantitative analysis. The meta-analysis included 3964 pregnancies with SCD and 336 559 controls. Compared with women without SCD, pregnancies complicated by SCD were at increased risk of pulmonary thromboembolism (relative risk (RR) 7.74; 95% CI 4.65 to 12.89). The estimated prevalence of acute chest syndrome and pneumonia was 6.46% (95% CI 4.66% to 8.25%), with no significant difference between the HbSS and HbSC genotypes (RR 1.42; 95% CI 0.90 to 2.23). INTERPRETATION: This meta-analysis highlighted a strong association between SCD and maternal pulmonary complications. Understanding the risks of and the factors associated with pulmonary complications would aid preconceptual counselling and optimal management of the condition in pregnancy, thereby reducing associated maternal morbidity and mortality. PROSPERO REGISTRATION NUMBER: CRD42019124708