{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/federated-multi-task-learning","title":"Federated Multi-Task Learning","arxiv_id":"1705.10467","date":"2017-05-30","proceeding":"NeurIPS 2017 12","authors":["Virginia Smith","Chao-Kai Chiang","Maziar Sanjabi","Ameet Talwalkar"],"abstract":"Federated learning poses new statistical and systems challenges in training\nmachine learning models over distributed networks of devices. In this work, we\nshow that multi-task learning is naturally suited to handle the statistical\nchallenges of this setting, and propose a novel systems-aware optimization\nmethod, MOCHA, that is robust to practical systems issues. Our method and\ntheory for the first time consider issues of high communication cost,\nstragglers, and fault tolerance for distributed multi-task learning. The\nresulting method achieves significant speedups compared to alternatives in the\nfederated setting, as we demonstrate through simulations on real-world\nfederated datasets.","url_abs":"http://arxiv.org/abs/1705.10467v2","url_pdf":"http://arxiv.org/pdf/1705.10467v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"federated-multi-task-learning","repo_url":"https://github.com/gingsmith/fmtl","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"federated-multi-task-learning","repo_url":"https://github.com/TsingZ0/PFL-Non-IID","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"federated-learning","task_name":"Federated Learning"},{"task_slug":"multi-task-learning","task_name":"Multi-Task Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1705.10467","atlas_url":"https://app.syntology.ai/?focus=1705.10467","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}