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Found 5 papers, 0 papers with code
Batch Clipping and Adaptive Layerwise Clipping for Differential Private Stochastic Gradient Descent
In this paper, we propose {\em a new ALC and provide rigorous DP proofs for both BC and ALC}.
Generalizing DP-SGD with Shuffling and Batch Clipping
Classical differential private DP-SGD implements individual clipping with random subsampling, which forces a mini-batch SGD approach.
Proactive DP: A Multple Target Optimization Framework for DP-SGD
Generally, DP-SGD is $(\epsilon\leq 1/2,\delta=1/N)$-DP if $\sigma=\sqrt{2(\epsilon +\ln(1/\delta))/\epsilon}$ with $T$ at least $\approx 2k^2/\epsilon$ and $(2/e)^2k^2-1/2\geq \ln(N)$, where $T$ is the total number of rounds, and $K=kN$ is the total number of gradient computations where $k$ measures $K$ in number of epochs of size $N$ of the local data set.
Hogwild! over Distributed Local Data Sets with Linearly Increasing Mini-Batch Sizes
We consider big data analysis where training data is distributed among local data sets in a heterogeneous way -- and we wish to move SGD computations to local compute nodes where local data resides.
Asynchronous Federated Learning with Reduced Number of Rounds and with Differential Privacy from Less Aggregated Gaussian Noise
The feasibility of federated learning is highly constrained by the server-clients infrastructure in terms of network communication.
