A federated recommendation algorithm based on user clustering and meta-learning

Federated recommendation is a typical application of federated learning, which can protect the privacy users by exchanging models between users' devices and central servers rather than users' raw data. Recently, although some research in federated recommendation has made remarkable progress, there are still two issues need to be addressed further due to the non -independent and identical distribution (Non-IID) data which is very common in federal recommendation systems. First, the communication load of the user device during training is heavy. Second, the trained local model lacks personalization. Aiming at the above problems, federated recommendation algorithm based on user clustering and meta -learning, ClusterFedMet, is proposed to improve communication efficiency and recommendation personalization simultaneously. In ClusterFedMet, users are clustered into different clusters according to their data distribution, and user sampling are performed based on the clustering result, thus reduce harmful interference among users with different data distribution. The model is trained with meta -learning, which can generate more personalized local models. During learning, a controller which can dynamically tune the hyperparameters for users is designed to achieve performance. According to weights, gradients, and losses of each step, the controller can find a learning suitable for each user's local data and model. We perform evaluations for the proposed algorithm on public datasets, and the results demonstrate that our algorithm outperforms other advanced methods in of recommendation accuracy and communication efficiency.