LightFR: Lightweight Federated Recommendation with Privacy-preserving Matrix Factorization. (arXiv:2206.11743v1 [cs.IR])

Federated recommender system (FRS), which enables many local devices to train
a shared model jointly without transmitting local raw data, has become a
prevalent recommendation paradigm with privacy-preserving advantages. However,
previous work on FRS performs similarity search via inner product in continuous
embedding space, which causes an efficiency bottleneck when the scale of items
is extremely large. We argue that such a scheme in federated settings ignores
the limited capacities in resource-constrained user devices (i.e., storage
space, computational overhead, and …

ir matrix privacy