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However, to be effective, these systems need to collect and analyze large volumes of personal data (e.g., location check-ins, movie ratings, click rates .. etc.), which exposes users to numerous privacy threats. In this context, recommender systems based on Federated Learning (FL) appear to be a promising solution for enforcing privacy as they compute accurate recommendations while keeping personal data on the users' devices. However, FL, and therefore FL-based recommender systems, rely on a central server that can experience scalability issues besides being vulnerable to attacks. To remedy this, we propose PEPPER, a decentralized recommender system based on gossip learning principles. In PEPPER, users gossip model updates and aggregate them asynchronously. At the heart of PEPPER reside two key components: a personalized peer-sampling protocol that keeps in the neighborhood of each node, a proportion of nodes that have similar interests to the former and a simple yet effective model aggregation function that builds a model that is better suited to each user. Through experiments on three real datasets implementing two use cases: a location check-in recommendation and a movie recommendation, we demonstrate that our solution converges up to 42% faster than with other decentralized solutions providing up to 9% improvement on average performance metric such as hit ratio and up to 21% improvement on long tail performance compared to decentralized competitors.<\/jats:p>","DOI":"10.1145\/3550302","type":"journal-article","created":{"date-parts":[[2022,9,7]],"date-time":"2022-09-07T14:54:27Z","timestamp":1662562467000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":28,"title":["PEPPER"],"prefix":"10.1145","volume":"6","author":[{"given":"Yacine","family":"Belal","sequence":"first","affiliation":[{"name":"INSA Lyon, France and LIRIS, Lyon, France"}]},{"given":"Aur\u00e9lien","family":"Bellet","sequence":"additional","affiliation":[{"name":"Univ. Lille, France and INRIA, France and CNRS, Lille, France"}]},{"given":"Sonia Ben","family":"Mokhtar","sequence":"additional","affiliation":[{"name":"INSA Lyon, France and LIRIS, France and CNRS, Lyon, France"}]},{"given":"Vlad","family":"Nitu","sequence":"additional","affiliation":[{"name":"INSA Lyon, France and LIRIS, France and CNRS, Lyon, France"}]}],"member":"320","published-online":{"date-parts":[[2022,9,7]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Mart\u00edn Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Greg S. 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[n.d.]. 235 Million Instagram TikTok And YouTube User Profiles Exposed In Massive Data Leak. https:\/\/www.forbes.com\/sites\/daveywinder\/2020\/08\/19\/massive-data-leak235-million-instagram-tiktok-and-youtube-user-profiles-exposed\/"},{"key":"e_1_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"e_1_2_1_67_1","volume-title":"International Conference on Machine Learning. PMLR, 5650--5659","author":"Yin Dong","year":"2018","unstructured":"Dong Yin, Yudong Chen, Ramchandran Kannan, and Peter Bartlett. 2018. Byzantine-robust distributed learning: Towards optimal statistical rates. In International Conference on Machine Learning. 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