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We introduce the payload optimization method for federated recommendersystems (FRS). In federated learning (FL), the global model payload that ismoved between the server and users depends on the number of items to recommend.The model payload grows when there is an increasing number of items. Thisbecomes challenging for an FRS if it is running in production mode. To tacklethe payload challenge, we formulated a multi-arm bandit solution that selectedpart of the global model and transmitted it to all users. The selection processwas guided by a novel reward function suitable for FL systems. So far as we areaware, this is the first optimization method that seeks to address itemdependent payloads. The method was evaluated using three benchmarkrecommendation datasets. The empirical validation confirmed that the proposedmethod outperforms the simpler methods that do not benefit from the bandits forthe purpose of item selection. In addition, we have demonstrated the usefulnessof our proposed method by rigorously evaluating the effects of a payloadreduction on the recommendation performance degradation. Our method achieved upto a 90\% reduction in model payload, yielding only a $\sim$4\% - 8\% loss inthe recommendation performance for highly sparse datasets

A Payload Optimization Method for Federated Recommender Systems