Federated　learning　has　been　a　paradigm　for　privacy-preserving　machine　learning,　but　recently　gradient　leakage　attacks　threaten　privacy　in　federated　learning.　Secure　aggregation　for　federated　learning　is　an　approach　to　protect　users’　privacy　from　these　attacks.　Especially　in　federated　learning　with　large-scale　mobile　devices,　e.g.,　smartphones,　secure　aggregation　should　be　dropout-resilient　and　have　low　communication　overhead　in　addition　to　protecting　privacy.　However,　existing　studies　still　suffer　from　performance　degradation　and　security　risk,　since　the　number　of　the　dropped　users　increases.　To　address　these　problems,　we　propose　an　effective　and　high　dropout-resilience　secure　aggregation　protocol　based　on　homomorphic　Pseudorandom　Generator　and　Paillier,　which　can　guarantee　privacy　while　tolerating　up　to　almost　50%　of　users　dropping　out　in　both　the　honest　but　curious　and　actively　malicious　settings,　and　the　performance　of　aggregation　in　computation　and　communication　is　independent　to　the　dropped　users.　To　further　improve　performance,　we　reduce　the　number　of　the　ciphertexts　through　a　homomorphic　Pseudorandom　Generator　in　the　multiplicative　group　of　integers,　and　decrease　the　running　time　of　the　server-side　aggregation　by　computing　discrete　logarithms　fast　in　Paillier.　Experimental　evaluation　shows　that　the　proposed　protocol　reduces　the　communication　overhead　by　3×　while　achieving　2×　computation　speedup　over　the　prior　dropout-resilience　scheme.　IEEE