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Nowadays there are more and more items available online, this makes it hardfor users to find items that they like. Recommender systems aim to find theitem who best suits the user, using his historical interactions. Depending onthe context, these interactions may be more or less sensitive and collectingthem brings an important problem concerning the users' privacy. Federatedsystems have shown that it is possible to make accurate and efficientrecommendations without storing users' personal information. However, thesesystems use instantaneous feedback from the user. In this report, we proposeDRIFT, a federated architecture for recommender systems, using implicitfeedback. Our learning model is based on a recent algorithm for recommendationwith implicit feedbacks SAROS. We aim to make recommendations as precise asSAROS, without compromising the users' privacy. In this report we show thatthanks to our experiments, but also thanks to a theoretical analysis on theconvergence. We have shown also that the computation time has a linearcomplexity with respect to the number of interactions made. Finally, we haveshown that our algorithm is secure, and participants in our federated systemcannot guess the interactions made by the user, except DOs that have the iteminvolved in the interaction.

DRIFT: A Federated Recommender System with Implicit Feedback on the Items