{"id":362,"date":"2015-09-07T20:57:49","date_gmt":"2015-09-08T00:57:49","guid":{"rendered":"https:\/\/cecas.clemson.edu\/ndcl\/?p=362"},"modified":"2015-09-15T15:05:17","modified_gmt":"2015-09-15T19:05:17","slug":"clock-synchronization-strategy-adopted-by-industry","status":"publish","type":"post","link":"https:\/\/cecas.clemson.edu\/ndcl\/clock-synchronization-strategy-adopted-by-industry\/","title":{"rendered":"Featured"},"content":{"rendered":"<h5 style=\"text-align: justify;\">\n\t\t<!-- MasterSlider -->\n\t\t<div id=\"P_MS69e9237a33a22\" class=\"master-slider-parent msl ms-parent-id-5\" style=\"max-width:800px;\">\n\n\t\t\t\n\t\t\t<!-- MasterSlider Main -->\n\t\t\t<div id=\"MS69e9237a33a22\" class=\"master-slider ms-skin-default\" >\n\t\t\t\t \t\t\t\t \n\t\t\t\t<div  class=\"ms-slide\" data-delay=\"3\" data-fill-mode=\"fill\"  >\n\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/cecas.clemson.edu\/ndcl\/wp-content\/plugins\/master-slider\/public\/assets\/css\/blank.gif\" alt=\"\" title=\"\" data-src=\"https:\/\/cecas.clemson.edu\/ndcl\/wp-content\/uploads\/2024\/04\/PNAS.png\" \/>\n\n\t\t\t\t\t<div class=\"ms-info\">Recent work on saddle-point avoidance for nonconvex optimization and machine learning has been accepted to PNAS, one of the most prestigious multidisciplinary journals<\/div>\n\n\n\t<div class=\"ms-thumb\" ><div class=\"ms-tab-context\"><div class=\"&quot;ms-tab-context&quot;\"><\/div><\/div>\n<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div  class=\"ms-slide\" data-delay=\"3\" data-fill-mode=\"fill\"  >\n\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/cecas.clemson.edu\/ndcl\/wp-content\/plugins\/master-slider\/public\/assets\/css\/blank.gif\" alt=\"\" title=\"\" data-src=\"https:\/\/cecas.clemson.edu\/ndcl\/wp-content\/uploads\/2024\/05\/ICML.png\" \/>\n\n\t\t\t\t\t<div class=\"ms-info\">Recent work on privacy protection for decentralized stochastic optimization has been accepted to 2024 <strong><em>International Conference on Machine Learning<\/em><\/strong> (ICML), a\u00a0leading international conference in machine learning. Bilevel stochastic optimization is an effective tool for solving many machine learning problems.<br \/><p>Details: Chen, Z, Wang Y Q. 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