13,761 research outputs found
GMSC: Updates of Advances on Its Therapy in Immunological Diseases
Mesenchymal stem cells (MSCs) derived from various tissues are multi-potency of self-renewal and differentiation into multi-lineages, including chondrocytes, adipocytes and osteoblasts in vitro and in vivo. In addition, these cells also display potent immune regulatory roles that benefit the treatment of inflammatory and autoimmune diseases. We and others have previously identified that human gingival-derived mesenchymal stem cells (GMSCs) not only share similar biological features, but also display some potential advantages compared to other MSC populations. In the chapter, we have discussed the discovery, phenotypic and functional characteristics, as well as updated the advances of these cell therapies in immunological diseases
Towards Fairer and More Efficient Federated Learning via Multidimensional Personalized Edge Models
Federated learning (FL) is an emerging technique that trains massive and
geographically distributed edge data while maintaining privacy. However, FL has
inherent challenges in terms of fairness and computational efficiency due to
the rising heterogeneity of edges, and thus usually results in sub-optimal
performance in recent state-of-the-art (SOTA) solutions. In this paper, we
propose a Customized Federated Learning (CFL) system to eliminate FL
heterogeneity from multiple dimensions. Specifically, CFL tailors personalized
models from the specially designed global model for each client jointly guided
by an online trained model-search helper and a novel aggregation algorithm.
Extensive experiments demonstrate that CFL has full-stack advantages for both
FL training and edge reasoning and significantly improves the SOTA performance
w.r.t. model accuracy (up to 7.2% in the non-heterogeneous environment and up
to 21.8% in the heterogeneous environment), efficiency, and FL fairness.Comment: 8 pages, 7 figure
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