Federated Learning (FL) is recently explored as a machine learning paradigm to communally gain generalizable knowledge from the data available in a collection of edge devices without the requirement to transfer the data. FL gives rise to the opportunity to train models on edge devices while preserving user's privacy as the data never leaves user's premises. In this paper, we introduce a simple yet efficient extension of FL for recommender systems to improve on personalization and discuss closely-related meta-learning algorithms. Compared to state-of-the-art federated recommenders, our proposed algorithm is simpler and more robust in real-life scenarios. Through experiments on benchmark data, we evaluate our algorithm in root mean squared error (RMSE) of user's rating prediction.

Recent analyses[2, 4, 5] report that many sectors of our economy and society are more and more guided by data-driven decision processes (e.g., health care, public administrations, etc.). As such, Data Intensive (DI) applications are becoming more and more important and critical. They must be fault-tolerant, they should scale with the amount of data, and be able to elastically leverage additional resources as and when these last ones are provided [3]. Moreover, they should be able to avoid data drops introduced in case of sudden overloads and should offer some Quality of Service (QoS) guarantees.