LOCKS: User Differentially Private and Federated Optimal Client Sampling. (arXiv:2212.13071v1 [cs.CR])

With changes in privacy laws, there is often a hard requirement for client
data to remain on the device rather than being sent to the server. Therefore,
most processing happens on the device, and only an altered element is sent to
the server. Such mechanisms are developed by leveraging differential privacy
and federated learning. Differential privacy adds noise to the client outputs
and thus deteriorates the quality of each iteration. This distributed setting
adds a layer of complexity and additional …

client locks