Federated Learning with Classifier Shift for Class Imbalance

Federated learning aims to learn a global model collaboratively while the training data belongs to different clients and is not allowed to be exchanged. However, the statistical heterogeneity challenge on non-IID data, such as class imbalance in classification, will cause client drift and significantly reduce the performance of the global model. This paper proposes a simple and effective approach named FedShift which adds the shift on the classifier output during the local training phase to alleviate the negative impact of class imbalance. We theoretically prove that the classifier shift in FedShift can make the local optimum consistent with the global optimum and ensure the convergence of the algorithm. Moreover, our experiments indicate that FedShift significantly outperforms the other state-of-the-art federated learning approaches on various datasets regarding accuracy and communication efficiency

On the Evaluation of Generative Models in Distributed Learning Tasks

The evaluation of deep generative models including generative adversarial networks (GANs) and diffusion models has been extensively studied in the literature. While the existing evaluation methods mainly target a centralized learning problem with training data stored by a single client, many applications of generative models concern distributed learning settings, e.g. the federated learning scenario, where training data are collected by and distributed among several clients. In this paper, we study the evaluation of generative models in distributed learning tasks with heterogeneous data distributions. First, we focus on the Fr\'echet inception distance (FID) and consider the following FID-based aggregate scores over the clients: 1) FID-avg as the mean of clients' individual FID scores, 2) FID-all as the FID distance of the trained model to the collective dataset containing all clients' data. We prove that the model rankings according to the FID-all and FID-avg scores could be inconsistent, which can lead to different optimal generative models according to the two aggregate scores. Next, we consider the kernel inception distance (KID) and similarly define the KID-avg and KID-all aggregations. Unlike the FID case, we prove that KID-all and KID-avg result in the same rankings of generative models. We perform several numerical experiments on standard image datasets and training schemes to support our theoretical findings on the evaluation of generative models in distributed learning problems.Comment: 17 pages, 10 figure

Comprehensive Metabolic Profiling of Euphorbiasteroid in Rats by Integrating UPLC-Q/TOF-MS and NMR as Well as Microbial Biotransformation

Euphorbiasteroid, a lathyrane-type diterpene from Euphorbiae semen (the seeds of Euphorbia lathyris L.), has been shown to have a variety of pharmacological effects such as anti-tumor and anti-obesity. This study aims to investigate the metabolic profiles of euphorbiasteroid in rats and rat liver microsomes (RLMs) and Cunninghamella elegans bio-110930 by integrating ultra-performance liquid chromatography-quadrupole time-of-flight-mass spectrometry (UPLC-Q/TOF-MS), UNIFI software, and NMR techniques. A total of 31 metabolites were identified in rats. Twelve metabolites (M1–M5, M8, M12–M13, M16, M24–M25, and M29) were matched to the metabolites obtained by RLMs incubation and the microbial transformation of C. elegans bio-110930 and their structures were exactly determined through analysis of NMR spectroscopic data. In addition, the metabolic pathways of euphorbiasteroid were then clarified, mainly including hydroxylation, hydrolysis, oxygenation, sulfonation, and glycosylation. Finally, three metabolites, M3 (20-hydroxyl euphorbiasteroid), M24 (epoxylathyrol) and M25 (15-deacetyl euphorbiasteroid), showed significant cytotoxicity against four human cell lines with IC50 values from 3.60 μM to 40.74 μM. This is the first systematic investigation into the in vivo metabolic pathways of euphorbiasteroid and the cytotoxicity of its metabolites, which will be beneficial for better predicting the metabolism profile of euphorbiasteroid in humans and understanding its possible toxic material basis

Astragalus polysaccharide ameliorated complex factor-induced chronic fatigue syndrome by modulating the gut microbiota and metabolites in mice

Chronic fatigue syndrome (CFS) is a debilitating disease with no symptomatic treatment. Astragalus polysaccharide (APS), a component derived from the traditional Chinese medicine A. membranaceus, has significant anti-fatigue activity. However, the mechanisms underlying the potential beneficial effects of APS on CFS remain poorly understood. A CFS model of 6-week-old C57BL/6 male mice was established using the multiple-factor method. These mice underwent examinations for behavior, oxidative stress and inflammatory indicators in brain and intestinal tissues, and ileum histomorphology. 16 S rDNA sequencing analysis indicated that APS regulated the abundance of gut microbiota and increased production of short chain fatty acids (SCFAs) and anti-inflammatory bacteria. In addition, APS reversed the abnormal expression of Nrf2, NF-κB, and their downstream factors in the brain-gut axis and alleviated the reduction in SCFAs in the cecal content caused by CFS. Further, APS modulated the changes in serum metabolic pathways induced by CFS. Finally, it was verified that butyrate exerted antioxidant and anti-inflammatory effects in neuronal cells. In conclusion, APS could increase the SCFAs content by regulating the gut microbiota, and SCFAs (especially butyrate) can further regulate the oxidative stress and inflammation in the brain, thus alleviating CFS. This study explored the efficacy and mechanism of APS for CFS from the perspective of gut-brain axis and provides a reference to further explore the efficacy of APS and the role of SCFAs in the central nervous system

Metabolism and pharmacokinetics of alantolactone and isoalantolactone in rats: Thiol conjugation as a potential metabolic pathway

Alantolactone (AL) and isoalantolactone (IAL), two major active sesquiterpene lactones isolated from Radix Inulae extract, have a wide range of pharmacological activities. The predominant metabolic pathway of AL and IAL observed was glutathione (GSH) conjugation in vitro, which could occur in the absence of metabolic enzymes. Non-enzymatic conjugation with cysteine (Cys) couldalso be observed. Four metabolites (AL-GSH, AL-Cys, IAL-GSH, IAL-Cys) were subsequently isolated and confirmed by nuclear magnetic resonance (NMR). The results indicated that the thiol of GSH or Cys can be reacted with the exomethylene carbon atoms of a, 13 unsaturated carbonyl of AL and IAL. After intravenous administration in rats, AL and IAL were extensively metabolized, and the exposure, as measured by area under the concentration-time curve (AUC), for AL-GSH, AL-Cys, IAL-GSH, and IAL-Cys was approximately 1.54-, 0.96-, 1.50-, and 0.91-fold that of the parent drug, respectively. The AUC ratio of metabolites to parent compounds of oral administration was 3.66-, 9.19-, 12.97-, and 9.92-fold that of the parent drug for the above metabolites, respectively. The bioavailability of AL-total (AL, AL-GSH, AL-Cys) and IAL-total (IAL, IAL-GSH, IAL-Cys) was, respectively, 8.39% and 13.07%, which was 3.62 and 6.95- fold that of AL (2.32%) and IAL (1.88%), respectively. The oral exposure will be underestimated if the parent drugs are tested alone. These findings provide useful information for preclinical safety evaluation, and for predicting AL and IAL metabolism in humans

Ultracompliant Heterogeneous Copper–Tin Nanowire Arrays Making a Supersolder

Due to the substantial increase in power density, thermal interface resistance that can constitute more than 50% of the total thermal resistance has generally become a bottleneck for thermal management in electronics. However, conventional thermal interface materials (TIMs) such as solder, epoxy, gel, and grease cannot fulfill the requirements of electronics for high-power and long-term operation. Here, we demonstrate a high-performance TIM consisting of a heterogeneous copper–tin nanowire array, which we term “supersolder” to emulate the role of conventional solders in bonding various surfaces. The supersolder is ultracompliant with a shear modulus 2–3 orders of magnitude lower than traditional solders and can reduce the thermal resistance by two times as compared with the state-of-the-art TIMs. This supersolder also exhibits excellent long-term reliability with >1200 thermal cycles over a wide temperature range. By resolving this critical thermal bottleneck, the supersolder enables electronic systems, ranging from microelectronics and portable electronics to massive data centers, to operate at lower temperatures with higher power density and reliability