  Building recommendation systems via federated learning (FL) is a new emerging
challenge for advancing next-generation Internet service and privacy
protection. Existing approaches train shared item embedding by FL while keeping
the user embedding private on client side. However, item embedding identical
for all clients cannot capture users' individual differences on perceiving the
same item and thus leads to poor personalization. Moreover, dense item
embedding in FL results in expensive communication cost and latency. To address
these challenges, we propose Federated Recommendation with Additive
Personalization (FedRAP), which learns a global view of items via FL and a
personalized view locally on each user. FedRAP enforces sparsity of the global
view to save FL's communication cost and encourages difference between the two
views through regularization. We propose an effective curriculum to learn the
local and global views progressively with increasing regularization weights. To
produce recommendations for an user, FedRAP adds the two views together to
obtain a personalized item embedding. FedRAP achieves the best performance in
FL setting on multiple benchmarks. It outperforms recent federated
recommendation methods and several ablation study baselines.
