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07/21/2023
Batch Clipping and Adaptive Layerwise Clipping for Differential Private Stochastic Gradient Descent
Each round in Differential Private Stochastic Gradient Descent (DPSGD) t...
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03/08/2023
Considerations on the Theory of Training Models with Differential Privacy
In federated learning collaborative learning takes place by a set of cli...
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12/12/2022
Generalizing DP-SGD with Shuffling and Batching Clipping
Classical differential private DP-SGD implements individual clipping wit...
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01/05/2022
Secure Remote Attestation with Strong Key Insulation Guarantees
Recent years have witnessed a trend of secure processor design in both a...
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02/17/2021
Differential Private Hogwild! over Distributed Local Data Sets
We consider the Hogwild! setting where clients use local SGD iterations ...
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10/27/2020
Hogwild! over Distributed Local Data Sets with Linearly Increasing Mini-Batch Sizes
Hogwild! implements asynchronous Stochastic Gradient Descent (SGD) where...
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07/17/2020
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 serve...
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06/18/2020
Beware the Black-Box: on the Robustness of Recent Defenses to Adversarial Examples
Recent defenses published at venues like NIPS, ICML, ICLR and CVPR are m...
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03/01/2020
A Hybrid Stochastic Policy Gradient Algorithm for Reinforcement Learning
We propose a novel hybrid stochastic policy gradient estimator by combin...
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02/19/2020
A Unified Convergence Analysis for Shuffling-Type Gradient Methods
In this paper, we provide a unified convergence analysis for a class of ...
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10/03/2019
BUZz: BUffer Zones for defending adversarial examples in image classification
We propose a novel defense against all existing gradient based adversari...
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01/22/2019
Optimal Finite-Sum Smooth Non-Convex Optimization with SARAH
The total complexity (measured as the total number of gradient computati...
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01/22/2019
DTN: A Learning Rate Scheme with Convergence Rate of O(1/t) for SGD
We propose a novel diminishing learning rate scheme, coined Decreasing-T...
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10/10/2018
Tight Dimension Independent Lower Bound on Optimal Expected Convergence Rate for Diminishing Step Sizes in SGD
We study convergence of Stochastic Gradient Descent (SGD) for strongly c...
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10/09/2018
Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD
We study Stochastic Gradient Descent (SGD) with diminishing step sizes f...
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02/11/2018
