{"id":4,"date":"2011-12-08T11:55:34","date_gmt":"2011-12-08T11:55:34","guid":{"rendered":"http:\/\/project.inria.fr\/template1\/?page_id=4"},"modified":"2026-03-17T17:29:28","modified_gmt":"2026-03-17T16:29:28","slug":"home","status":"publish","type":"page","link":"https:\/\/project.inria.fr\/learnnet\/","title":{"rendered":"Project Overview"},"content":{"rendered":"<p><strong>We currently have an open postdoctoral position on inference delivery systems. For more information, contact <a href=\"https:\/\/nadach.github.io\">N. Achir<\/a>.<\/strong><\/p>\n\n\n\n<p>The Learn-Net project is a collaboration between Nokia and Inria coordinated by Alberto Conte (Nokia Bell Labs) and Malcolm Egan (Inria). Learn-Net is at the interface between networking and machine learning, aiming to support distributed machine learning (e.g., federated learning) and AI-native networks. The project involves 7 Inria and 2 Nokia teams with expertise in networking, privacy, and machine learning. The project hosts 4 PhD candidates and 2 postdoctoral researchers.<\/p>\n\n\n\n<p>The Learn-Net project is organized in three axes:<\/p>\n\n\n\n<p><strong>Axis 1: Inference Delivery Networks<\/strong>. In this axis, solutions to integrate inference delivery throughout the infrastructure continuum (access, edge, regional data center, cloud) are explored. <\/p>\n\n\n\n<p><strong>Axis 2: Heterogeneous Learning.<\/strong> In this axis, deep learning techniques are tailored to heterogeneous architectures, heterogeneous data, and heterogeneous hidden models.<\/p>\n\n\n\n<p><strong>Axis 3: Dataflow Optimization. <\/strong>In this axis, new joint communication and learning strategies are investigated to satisfy constraints on privacy, energy, bandwidth, latency, and reliability.<\/p>\n\n\n\n<p>The partners in the project are listed <a href=\"https:\/\/project.inria.fr\/learnnet\/project-partners\/\" data-type=\"page\" data-id=\"83\">here<\/a>.<\/p>\n\n\n\n<p>Publications from this project are listed <a href=\"https:\/\/project.inria.fr\/learnnet\/publications\/\" data-type=\"page\" data-id=\"89\">here<\/a>.<\/p>","protected":false},"excerpt":{"rendered":"<p>We currently have an open postdoctoral position on inference delivery systems. For more information, contact N. Achir. The Learn-Net project is a collaboration between Nokia and Inria coordinated by Alberto Conte (Nokia Bell Labs) and Malcolm Egan (Inria). Learn-Net is at the interface between networking and machine learning, aiming to\u2026<\/p>\n<p> <a class=\"continue-reading-link\" href=\"https:\/\/project.inria.fr\/learnnet\/\"><span>Continue reading<\/span><i class=\"crycon-right-dir\"><\/i><\/a> <\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"open","template":"","meta":{"footnotes":""},"class_list":["post-4","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/project.inria.fr\/learnnet\/wp-json\/wp\/v2\/pages\/4","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/project.inria.fr\/learnnet\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/project.inria.fr\/learnnet\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/project.inria.fr\/learnnet\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/project.inria.fr\/learnnet\/wp-json\/wp\/v2\/comments?post=4"}],"version-history":[{"count":10,"href":"https:\/\/project.inria.fr\/learnnet\/wp-json\/wp\/v2\/pages\/4\/revisions"}],"predecessor-version":[{"id":110,"href":"https:\/\/project.inria.fr\/learnnet\/wp-json\/wp\/v2\/pages\/4\/revisions\/110"}],"wp:attachment":[{"href":"https:\/\/project.inria.fr\/learnnet\/wp-json\/wp\/v2\/media?parent=4"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}