  Federated recommendation aims to collect global knowledge by aggregating
local models from massive devices, to provide recommendations while ensuring
privacy. Current methods mainly leverage aggregation functions invented by
federated vision community to aggregate parameters from similar clients, e.g.,
clustering aggregation. Despite considerable performance, we argue that it is
suboptimal to apply them to federated recommendation directly. This is mainly
reflected in the disparate model architectures. Different from structured
parameters like convolutional neural networks in federated vision, federated
recommender models usually distinguish itself by employing one-to-one item
embedding table. Such a discrepancy induces the challenging embedding skew
issue, which continually updates the trained embeddings but ignores the
non-trained ones during aggregation, thus failing to predict future items
accurately. To this end, we propose a personalized Federated recommendation
model with Composite Aggregation (FedCA), which not only aggregates similar
clients to enhance trained embeddings, but also aggregates complementary
clients to update non-trained embeddings. Besides, we formulate the overall
learning process into a unified optimization algorithm to jointly learn the
similarity and complementarity. Extensive experiments on several real-world
datasets substantiate the effectiveness of our proposed model. The source codes
are available at https://github.com/hongleizhang/FedCA.
