  To prevent private training data leakage in Fed?erated Learning systems, we
propose a novel se?cure aggregation scheme based on seed homomor?phic
pseudo-random generator (SHPRG), named SASH. SASH leverages the homomorphic
property of SHPRG to simplify the masking and demask?ing scheme, which for each
of the clients and for the server, entails an overhead linear w.r.t model size
and constant w.r.t number of clients. We prove that even against worst-case
colluding adversaries, SASH preserves training data privacy, while being
resilient to dropouts without extra overhead. We experimentally demonstrate
SASH significantly improves the efficiency to 20 times over baseline,
especially in the more realistic case where the numbers of clients and model
size become large, and a cer?tain percentage of clients drop out from the
system.
