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 of 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 major 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, a 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 meta-learning, a controller which can dynamically tune the hyperparameters for users is designed to achieve better performance. According to weights, gradients, and losses of each step, the controller can find a learning rate suitable for each user's local data and model. We perform evaluations for the proposed algorithm on two public datasets, and the results demonstrate that our algorithm outperforms other advanced methods in terms of recommendation accuracy and communication efficiency.