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1. Stochastic ADMM Based Distributed Machine Learning with Differential Privacy

   https://doi.org/10.1007/978-3-030-37228-6_13  

   Ding, Jiahao; Errapotu, Sai Mounika; Zhang, Haijun; Gong, Yanmin; Pan, Miao; Han, Zhu (December 2019, Lecture notes of the Institute for Computer Sciences Social Informatics and Telecommunications Engineering)

   While embracing various machine learning techniques to make effective decisions in the big data era, preserving the privacy of sensitive data poses significant challenges. In this paper, we develop a privacy-preserving distributed machine learning algorithm to address this issue. Given the assumption that each data provider owns a dataset with different sample size, our goal is to learn a common classifier over the union of all the local datasets in a distributed way without leaking any sensitive information of the data samples. Such an algorithm needs to jointly consider efficient distributed learning and effective privacy preservation. In the proposed algorithm, we extend stochastic alternating direction method of multipliers (ADMM) in a distributed setting to do distributed learning. For preserving privacy during the iterative process, we combine differential privacy and stochastic ADMM together. In particular, we propose a novel stochastic ADMM based privacy-preserving distributed machine learning (PS-ADMM) algorithm by perturbing the updating gradients, that provide differential privacy guarantee and have a low computational cost. We theoretically demonstrate the convergence rate and utility bound of our proposed PS-ADMM under strongly convex objective. Through our experiments performed on real-world datasets, we show that PS-ADMM outperforms other differentially private ADMM algorithms under the same differential privacy guarantee. 

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2. Improving the Privacy and Accuracy of ADMM-Based Distributed Algorithms

   Zhang, Xueru; Khalili, Mohammad Mahdi; Liu, Mingyan (January 2018, International Conference on Machine Learning)

   Alternating direction method of multiplier (ADMM) is a popular method used to design distributed versions of a machine learning algorithm, whereby local computations are performed on local data with the output exchanged among neighbors in an iterative fashion. During this iterative process the leakage of data privacy arises. A differentially private ADMM was proposed in prior work (Zhang & Zhu, 2017) where only the privacy loss of a single node during one iteration was bounded, a method that makes it difficult to balance the tradeoff between the utility attained through distributed computation and privacy guarantees when considering the total privacy loss of all nodes over the entire iterative process. We propose a perturbation method for ADMM where the perturbed term is correlated with the penalty parameters; this is shown to improve the utility and privacy simultaneously. The method is based on a modified ADMM where each node independently determines its own penalty parameter in every iteration and decouples it from the dual updating step size. The condition for convergence of the modified ADMM and the lower bound on the convergence rate are also derived. 

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3. Learning Sparse High-Dimensional Matrix-Valued Graphical Models From Dependent Data

   https://doi.org/10.1109/TSP.2024.3395897  

   Tugnait, Jitendra K (May 2024, IEEE Transactions on Signal Processing)

   We consider the problem of inferring the conditional independence graph (CIG) of a sparse, high-dimensional, stationary matrix-variate Gaussian time series. All past work on high-dimensional matrix graphical models assumes that independent and identically distributed (i.i.d.) observations of the matrix-variate are available. Here we allow dependent observations. We consider a sparse-group lasso-based frequency-domain formulation of the problem with a Kronecker-decomposable power spectral density (PSD), and solve it via an alternating direction method of multipliers (ADMM) approach. The problem is biconvex which is solved via flip-flop optimization. We provide sufficient conditions for local convergence in the Frobenius norm of the inverse PSD estimators to the true value. This result also yields a rate of convergence. We illustrate our approach using numerical examples utilizing both synthetic and real data. 

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4. Recycled ADMM: Improve Privacy and Accuracy with Less Computation in Distributed Algorithms

   Zhang, Xueru; Khalili, Mohammad Mahdi; Liu, Mingyan (January 2018, Annual Allerton Conference on Control, Communication, and Computing)

   Alternating direction method of multiplier (ADMM) is a powerful method to solve decentralized convex optimization problems. In distributed settings, each node performs computation with its local data and the local results are exchanged among neighboring nodes in an iterative fashion. During this iterative process the leakage of data privacy arises and can accumulate significantly over many iterations, making it difficult to balance the privacy-utility tradeoff. In this study we propose Recycled ADMM (R-ADMM), where a linear approximation is applied to every even iteration, its solution directly calculated using only results from the previous, odd iteration. It turns out that under such a scheme, half of the updates incur no privacy loss and require much less computation compared to the conventional ADMM. We obtain a sufficient condition for the convergence of R-ADMM and provide the privacy analysis based on objective perturbation. 

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5. MicroLED light source for optical sectioning structured illumination microscopy

   https://doi.org/10.1364/OE.486754  

   Kumar, Vikrant; Behrman, Keith; Speed, Forest; Saladrigas, Catherine_A; Supekar, Omkar; Huang, Zicong; Bright, Victor_M; Welle, Cristin_G; Restrepo, Diego; Gopinath, Juliet_T; et al (May 2023, Optics Express)

   Optical sectioning structured illumination microscopy (OS-SIM) provides optical sectioning capability in wide-field microscopy. The required illumination patterns have traditionally been generated using spatial light modulators (SLM), laser interference patterns, or digital micromirror devices (DMDs) which are too complex to implement in miniscope systems. MicroLEDs have emerged as an alternative light source for patterned illumination due to their extreme brightness capability and small emitter sizes. This paper presents a directly addressable striped microLED microdisplay with 100 rows on a flexible cable (70 cm long) for use as an OS-SIM light source in a benchtop setup. The overall design of the microdisplay is described in detail with luminance-current-voltage characterization. OS-SIM implementation with a benchtop setup shows the optical sectioning capability of the system by imaging within a 500 µm thick fixed brain slice from a transgenic mouse where oligodendrocytes are labeled with a green fluorescent protein (GFP). Results show improved contrast in reconstructed optically sectioned images of 86.92% (OS-SIM) compared with 44.31% (pseudo-widefield). MicroLED based OS-SIM therefore offers a new capability for deep tissue widefield imaging. 

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