Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction

Due to limited communication capacities of edge devices, most existing federated learning (FL) methods randomly select only a subset of devices to participate in training for each communication round. Compared with engaging all the available clients, the random-selection mechanism can lead to significant performance degradation on non-IID (independent and identically distributed) data. In this paper, we show our key observation that the essential reason resulting in such performance degradation is the class-imbalance of the grouped data from randomly selected clients. Based on our key observation, we design an efficient heterogeneity-aware client sampling mechanism, i.e., Federated Class-balanced Sampling (Fed-CBS), which can effectively reduce class-imbalance of the group dataset from the intentionally selected clients. In particular, we propose a measure of class-imbalance and then employ homomorphic encryption to derive this measure in a privacy-preserving way. Based on this measure, we also design a computation-efficient client sampling strategy, such that the actively selected clients will generate a more class-balanced grouped dataset with theoretical guarantees. Extensive experimental results demonstrate Fed-CBS outperforms the status quo approaches. Furthermore, it achieves comparable or even better performance than the ideal setting where all the available clients participate in the FL training

Hyper-Activated Pro-Inflammatory CD16+ Monocytes Correlate with the Severity of Liver Injury and Fibrosis in Patients with Chronic Hepatitis B

BACKGROUND: Extensive mononuclear cell infiltration is strongly correlated with liver damage in patients with chronic hepatitis B virus (CHB) infection. Macrophages and infiltrating monocytes also participate in the development of liver damage and fibrosis in animal models. However, little is known regarding the immunopathogenic role of peripheral blood monocytes and intrahepatic macrophages. METHODOLOGY/PRINCIPAL FINDINGS: The frequencies, phenotypes, and functions of peripheral blood and intrahepatic monocyte/macrophage subsets were analyzed in 110 HBeAg positive CHB patients, including 32 immune tolerant (IT) carriers and 78 immune activated (IA) patients. Liver biopsies from 20 IA patients undergoing diagnosis were collected for immunohistochemical analysis. IA patients displayed significant increases in peripheral blood monocytes and intrahepatic macrophages as well as CD16(+) subsets, which were closely associated with serum alanine aminotransferase (ALT) levels and the liver histological activity index (HAI) scores. In addition, the increased CD16(+) monocytes/macrophages expressed higher levels of the activation marker HLA-DR compared with CD16(-) monocytes/macrophages. Furthermore, peripheral blood CD16(+) monocytes preferentially released inflammatory cytokines and hold higher potency in inducing the expansion of Th17 cells. Of note, hepatic neutrophils also positively correlated with HAI scores. CONCLUSIONS: These distinct properties of monocyte/macrophage subpopulations participate in fostering the inflammatory microenvironment and liver damage in CHB patients and further represent a collaborative scenario among different cell types contributing to the pathogenesis of HBV-induced liver disease

A relational model of perceived overqualification : the moderating role of interpersonal influence on social acceptance.

Theories of perceived overqualification have tended to focus on employees’ job-related responses to account for effects on performance. We offer an alternative perspective and theorize that perceived overqualification could influence work performance through a relational mechanism. We propose that relational skills, in the form of interpersonal influence of overqualified employees, determine their tendency to experience social acceptance and, thus, engage in positive work-related behaviors. We tested this relational model across two studies using time-lagged, multisource data. In Study 1, the results indicated that for employees high on interpersonal influence, perceived overqualification was positively related to self-reported social acceptance, whereas for employees low on interpersonal influence, the relationship was negative. Social acceptance, in turn, was positively related to in-role job performance, interpersonal altruism, and team member proactivity evaluated by supervisors. In Study 2, we focused on peer-reported social acceptance and found that the indirect relationships between perceived overqualification and supervisor-reported behavioral outcomes via social acceptance were negative when interpersonal influence was low and nonsignificant when interpersonal influence was high. The implications of the general findings are discussed

Boosting Large-scale Parallel Training Efficiency with C4: A Communication-Driven Approach

The emergence of Large Language Models (LLMs) has necessitated the adoption of parallel training techniques, involving the deployment of thousands of GPUs to train a single model. Unfortunately, we have found that the efficiency of current parallel training is often suboptimal, largely due to the following two main issues. Firstly, hardware failures are inevitable, leading to interruptions in the training tasks. The inability to quickly identify the faulty components results in a substantial waste of GPU resources. Secondly, since GPUs must wait for parameter synchronization to complete before proceeding to the next round of computation, network congestions can greatly increase the waiting time for GPUs. To address these challenges, this paper introduces a communication-driven solution, namely the C4. The key insights of C4 are two folds. First, in parallel training, collective communication exhibits periodic and homogeneous characteristics, so any anomalies are certainly due to some form of hardware malfunction. By leveraging this feature, C4 can rapidly identify the faulty components, swiftly isolate the anomaly, and restart the task, thereby avoiding resource wastage caused by delays in anomaly detection. Second, the predictable communication model of collective communication, involving few large flows, allows C4 to efficiently execute traffic planning, substantially reducing network congestion. C4 has been extensively implemented across our production systems, cutting error-induced overhead by roughly 30% and enhancing runtime performance by about 15% for certain applications with moderate communication costs

Versican G3 Promotes Mouse Mammary Tumor Cell Growth, Migration, and Metastasis by Influencing EGF Receptor Signaling

Increased versican expression in breast tumors is predictive of relapse and has negative impact on survival rates. The C-terminal G3 domain of versican influences local and systemic tumor invasiveness in pre-clinical murine models. However, the mechanism(s) by which G3 influences breast tumor growth and metastasis is not well characterized. Here we evaluated the expression of versican in mouse mammary tumor cell lines observing that 4T1 cells expressed highest levels while 66c14 cells expressed low levels. We exogenously expressed a G3 construct in 66c14 cells and analyzed its effects on cell proliferation, migration, cell cycle progression, and EGFR signaling. Experiments in a syngeneic orthotopic animal model demonstrated that G3 promoted tumor growth and systemic metastasis in vivo. Activation of pERK correlated with high levels of G3 expression. In vitro, G3 enhanced breast cancer cell proliferation and migration by up-regulating EGFR signaling, and enhanced cell motility through chemotactic mechanisms to bone stromal cells, which was prevented by inhibitor AG 1478. G3 expressing cells demonstrated increased CDK2 and GSK-3β (S9P) expression, which were related to cell growth. The activity of G3 on mouse mammary tumor cell growth, migration and its effect on spontaneous metastasis to bone in an orthotopic model was modulated by up-regulating the EGFR-mediated signaling pathway. Taken together, EGFR-signaling appears to be an important pathway in versican G3-mediated breast cancer tumor invasiveness and metastasis

Chronic cerebral hypoperfusion induces venous dysfunction via EPAS1 regulation in mice

Vascular dementia is the second most common form of dementia. Yet, the mechanisms by which cerebrovascular damage progresses are insufficiently understood. Here, we create bilateral common carotid artery stenosis in mice, which effectively impairs blood flow to the brain, a major cause of the disease. Through imaging and single-cell transcriptomics of the mouse cortex, we uncover that blood vessel venous cells undergo maladaptive structural changes associated with increased Epas1 expression and activation of developmental angiogenic pathways. In a human cell model comparing arterial and venous cells, we observe that low-oxygen condition leads to sustained EPAS1 signaling specifically in venous cells. EPAS1 inhibition reduces cerebrovascular abnormalities, microglial activation, and improves markers of cerebral perfusion in vivo. In human subjects, levels of damaged endothelial cells from venous vessels are correlated with white matter injury in the brain and poorer cognitive functions. Together, these findings indicate EPAS1 as a potential therapeutic target to restore cerebrovascular integrity and mitigate neuroinflammation