{"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/personalized-federated-learning-through-local","title":"Personalized Federated Learning through Local Memorization","arxiv_id":"2111.09360","date":"2021-11-17","proceeding":null,"authors":["Othmane Marfoq","Giovanni Neglia","Laetitia Kameni","Richard Vidal"],"abstract":"Federated learning allows clients to collaboratively learn statistical models while keeping their data local. 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