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Server-based\ntraining using stochastic gradient descent is compared with training on client\ndevices using the Federated Averaging algorithm. The federated algorithm, which\nenables training on a higher-quality dataset for this use case, is shown to\nachieve better prediction recall. This work demonstrates the feasibility and\nbenefit of training language models on client devices without exporting\nsensitive user data to servers. The federated learning environment gives users\ngreater control over the use of their data and simplifies the task of\nincorporating privacy by default with distributed training and aggregation\nacross a population of client devices.","url_abs":"http://arxiv.org/abs/1811.03604v2","url_pdf":"http://arxiv.org/pdf/1811.03604v2.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-mobile-keyboard","repo_url":"https://github.com/LeoSerena/MSThesis","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"federated-learning-for-mobile-keyboard","repo_url":"https://github.com/MsAmberWelch/Privacy-Engineering","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"federated-learning-for-mobile-keyboard","repo_url":"https://github.com/google-parfait/tensorflow-federated","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"federated-learning-for-mobile-keyboard","repo_url":"https://github.com/gregor160300/federated","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"federated-learning-for-mobile-keyboard","repo_url":"https://github.com/steph-jung/GenieType","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"federated-learning-for-mobile-keyboard","repo_url":"https://github.com/tensorflow/federated","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"federated-learning","task_name":"Federated Learning"},{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"prediction","task_name":"Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1811.03604","atlas_url":"https://app.syntology.ai/?focus=1811.03604","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.03604"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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