  In this work, we consider the problem of designing secure and efficient
federated learning (FL) frameworks. Existing solutions either involve a trusted
aggregator or require heavyweight cryptographic primitives, which degrades
performance significantly. Moreover, many existing secure FL designs work only
under the restrictive assumption that none of the clients can be dropped out
from the training protocol. To tackle these problems, we propose SEFL, a secure
and efficient FL framework that (1) eliminates the need for the trusted
entities; (2) achieves similar and even better model accuracy compared with
existing FL designs; (3) is resilient to client dropouts. Through extensive
experimental studies on natural language processing (NLP) tasks, we demonstrate
that the SEFL achieves comparable accuracy compared to existing FL solutions,
and the proposed pruning technique can improve runtime performance up to 13.7x.
