  Federated learning (FL) is a collaborative learning paradigm for
decentralized private data from mobile terminals (MTs). However, it suffers
from issues in terms of communication, resource of MTs, and privacy. Existing
privacy-preserving FL methods usually adopt the instance-level differential
privacy (DP), which provides a rigorous privacy guarantee but with several
bottlenecks: severe performance degradation, transmission overhead, and
resource constraints of edge devices such as MTs. To overcome these drawbacks,
we propose Fed-LTP, an efficient and privacy-enhanced FL framework with
\underline{\textbf{L}}ottery \underline{\textbf{T}}icket
\underline{\textbf{H}}ypothesis (LTH) and zero-concentrated
D\underline{\textbf{P}} (zCDP). It generates a pruned global model on the
server side and conducts sparse-to-sparse training from scratch with zCDP on
the client side. On the server side, two pruning schemes are proposed: (i) the
weight-based pruning (LTH) determines the pruned global model structure; (ii)
the iterative pruning further shrinks the size of the pruned model's
parameters. Meanwhile, the performance of Fed-LTP is also boosted via model
validation based on the Laplace mechanism. On the client side, we use
sparse-to-sparse training to solve the resource-constraints issue and provide
tighter privacy analysis to reduce the privacy budget. We evaluate the
effectiveness of Fed-LTP on several real-world datasets in both independent and
identically distributed (IID) and non-IID settings. The results clearly confirm
the superiority of Fed-LTP over state-of-the-art (SOTA) methods in
communication, computation, and memory efficiencies while realizing a better
utility-privacy trade-off.
