27 research outputs found
Fast-Convergent Federated Learning
Federated learning has emerged recently as a promising solution for
distributing machine learning tasks through modern networks of mobile devices.
Recent studies have obtained lower bounds on the expected decrease in model
loss that is achieved through each round of federated learning. However,
convergence generally requires a large number of communication rounds, which
induces delay in model training and is costly in terms of network resources. In
this paper, we propose a fast-convergent federated learning algorithm, called
FOLB, which performs intelligent sampling of devices in each round of model
training to optimize the expected convergence speed. We first theoretically
characterize a lower bound on improvement that can be obtained in each round if
devices are selected according to the expected improvement their local models
will provide to the current global model. Then, we show that FOLB obtains this
bound through uniform sampling by weighting device updates according to their
gradient information. FOLB is able to handle both communication and computation
heterogeneity of devices by adapting the aggregations according to estimates of
device's capabilities of contributing to the updates. We evaluate FOLB in
comparison with existing federated learning algorithms and experimentally show
its improvement in trained model accuracy, convergence speed, and/or model
stability across various machine learning tasks and datasets.Comment: 17 page
De novo mutations in SMCHD1 cause Bosma arhinia microphthalmia syndrome and abrogate nasal development
Bosma arhinia microphthalmia syndrome (BAMS) is an extremely rare and striking condition characterized by complete absence of the nose with or without ocular defects. We report here that missense mutations in the epigenetic regulator SMCHD1 mapping to the extended ATPase domain of the encoded protein cause BAMS in all 14 cases studied. All mutations were de novo where parental DNA was available. Biochemical tests and in vivo assays in Xenopus laevis embryos suggest that these mutations may behave as gain-of-function alleles. This finding is in contrast to the loss-of-function mutations in SMCHD1 that have been associated with facioscapulohumeral muscular dystrophy (FSHD) type 2. Our results establish SMCHD1 as a key player in nasal development and provide biochemical insight into its enzymatic function that may be exploited for development of therapeutics for FSHD
SAMU: design and implementation of frequency selectivity-aware multi-user MIMO for WLANs
Immigrant entrepreneurship on the move: a longitudinal analysis of first- and second-generation immigrant entrepreneurship in the Netherlands
Contains fulltext :
112975.pdf (publisher's version ) (Closed access)26 juni 201338 p