{"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-learning-for-keyword-spotting","title":"Federated Learning for Keyword Spotting","arxiv_id":"1810.05512","date":"2018-10-09","proceeding":null,"authors":["David Leroy","Alice Coucke","Thibaut Lavril","Thibault Gisselbrecht","Joseph Dureau"],"abstract":"We propose a practical approach based on federated learning to solve\nout-of-domain issues with continuously running embedded speech-based models\nsuch as wake word detectors. We conduct an extensive empirical study of the\nfederated averaging algorithm for the \"Hey Snips\" wake word based on a\ncrowdsourced dataset that mimics a federation of wake word users. We\nempirically demonstrate that using an adaptive averaging strategy inspired from\nAdam in place of standard weighted model averaging highly reduces the number of\ncommunication rounds required to reach our target performance. The associated\nupstream communication costs per user are estimated at 8 MB, which is a\nreasonable in the context of smart home voice assistants. Additionally, the\ndataset used for these experiments is being open sourced with the aim of\nfostering further transparent research in the application of federated learning\nto speech data.","url_abs":"http://arxiv.org/abs/1810.05512v4","url_pdf":"http://arxiv.org/pdf/1810.05512v4.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-learning-for-keyword-spotting","repo_url":"https://github.com/snipsco/keyword-spotting-research-datasets","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"federated-learning-for-keyword-spotting","repo_url":"https://github.com/sonos/keyword-spotting-research-datasets","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"federated-learning","task_name":"Federated Learning"},{"task_slug":"keyword-spotting","task_name":"Keyword Spotting"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1810.05512","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}