Efficient Federated Learning with Enhanced Privacy via Lottery Ticket Pruning in Edge Computing. (arXiv:2305.01387v1 [cs.DC])

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 …

communication computing data decentralized differential privacy edge edge computing federated learning instance mobile mts paradigm privacy private private data terms