SecEmb is a two-server protocol using function secret sharing that privately retrieves and aggregates only sparse item-embedding updates in federated recommender systems, cutting user communication and computation.
Towards federated learning at scale: System design
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SecEmb: Sparsity-Aware Secure Federated Learning of On-Device Recommender System with Large Embedding
SecEmb is a two-server protocol using function secret sharing that privately retrieves and aggregates only sparse item-embedding updates in federated recommender systems, cutting user communication and computation.