{"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/linear-speedup-in-personalized-collaborative","title":"Linear Speedup in Personalized Collaborative Learning","arxiv_id":"2111.05968","date":"2021-11-10","proceeding":null,"authors":["El Mahdi Chayti","Sai Praneeth Karimireddy","Sebastian U. Stich","Nicolas Flammarion","Martin Jaggi"],"abstract":"Collaborative training can improve the accuracy of a model for a user by trading off the model's bias (introduced by using data from other users who are potentially different) against its variance (due to the limited amount of data on any single user). In this work, we formalize the personalized collaborative learning problem as a stochastic optimization of a task 0 while giving access to N related but different tasks 1,..., N. We provide convergence guarantees for two algorithms in this setting -- a popular collaboration method known as weighted gradient averaging, and a novel bias correction method -- and explore conditions under which we can achieve linear speedup w.r.t. the number of auxiliary tasks N. Further, we also empirically study their performance confirming our theoretical insights.","url_abs":"https://arxiv.org/abs/2111.05968v4","url_pdf":"https://arxiv.org/pdf/2111.05968v4.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":"linear-speedup-in-personalized-collaborative","repo_url":"https://github.com/elmahdichayti/linspeedupcode","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"federated-learning","task_name":"Federated Learning"},{"task_slug":"stochastic-optimization","task_name":"Stochastic Optimization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2111.05968","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}