A local-training algorithm for nonconvex distributed optimization achieves communication efficiency and differential privacy via gradient clipping plus additive noise, with proven convergence to a stationary point within bounded distance and formal privacy guarantees.
On the output redundancy of LTI systems: A geometric approach with application to privacy
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Communication-Efficient Distributed Learning with Differential Privacy
A local-training algorithm for nonconvex distributed optimization achieves communication efficiency and differential privacy via gradient clipping plus additive noise, with proven convergence to a stationary point within bounded distance and formal privacy guarantees.