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1. Exact Distributed Stochastic Block Partitioning

   https://doi.org/10.1109/CLUSTER52292.2023.00010  

   Wanye, Frank; Gleyzer, Vitaliy; Kao, Edward; Feng, Wu-Chun (October 2023, IEEE)

   Stochastic block partitioning (SBP) is a community detection algorithm that is highly accurate even on graphs with a complex community structure, but its inherently serial nature hinders its widespread adoption by the wider scientific community. To make it practical to analyze large real-world graphs with SBP, there is a growing need to parallelize and distribute the algorithm. The current state-of-the-art distributed SBP algorithm is a divide-and-conquer approach that limits communication between compute nodes until the end of inference. This leads to the breaking of computational dependencies, which causes convergence issues as the number of compute nodes increases and when the graph is sufficiently sparse. To address this shortcoming, we introduce EDiSt — an exact distributed stochastic block partitioning algorithm. Under EDiSt, compute nodes periodically share community assignments during inference. Due to this additional communication, EDiSt improves upon the divide-and-conquer algorithm by allowing it to scale out to a larger number of compute nodes without suffering from convergence issues, even on sparse graphs. We show that EDiSt provides speedups of up to 26.9x over the divide-and-conquer approach and speedups up to 44.0x over shared memory parallel SBP when scaled out to 64 compute nodes. 

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2. Decentralized Stochastic Control of Distributed Energy Resources

   https://doi.org/10.1109/TPWRS.2017.2700472  

   Lin, Weixuan; Bitar, Eilyan (May 2017, IEEE Transactions on Power Systems)

   We consider the decentralized control of radial distribution systems with controllable photovoltaic inverters and energy storage resources. For such systems, we investigate the problem of designing fully decentralized controllers that minimize the expected cost of balancing demand, while guaranteeing the satisfaction of individual resource and distribution system voltage constraints. Employing a linear approximation of the branch flow model, we formulate this problem as the design of a decentralized disturbance-feedback controller that minimizes the expected value of a convex quadratic cost function, subject to robust convex quadratic constraints on the system state and input. As such problems are, in general, computationally intractable, we derive a tractable inner approximation to this decentralized control problem, which enables the efficient computation of an affine control policy via the solution of a finite-dimensional conic program. As affine policies are, in general, suboptimal for the family of systems considered, we provide an efficient method to bound their suboptimality via the optimal solution of another finite-dimensional conic program. A case study of a 12 kV radial distribution system demonstrates that decentralized affine controllers can perform close to optimal. 

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3. Distributed Stochastic Optimization of Network Function Virtualization

   https://doi.org/10.1109/GLOCOM.2017.8254728  

   Chen, Xiaojing; Ni, Wei; Chen, Tianyi; Collings, Iain B.; Wang, Xin; Liu, Ren Ping; Giannakis, Georgios B. (December 2017, IEEE Global Communications Conference)
4. Stochastic First-Order Methods Over Distributed Data

   https://doi.org/10.1109/SAM53842.2022.9827792  

   Qureshi, Muhammad I.; Khan, Usman A. (June 2022, 2022 IEEE 12th Sensor Array and Multichannel Signal Processing Workshop (SAM))
5. Distributed Stochastic Optimization of Network Function Virtualization

   Chen, X.; Ni, W.; Chen, T.; Collins, I. B.; Liu, R. P.; Giannakis, G. B. (December 2017, Proc. of Globecom Conf.)