| 1. |
- Dawoud, Mohammed M., et al.
(author)
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Differentially Private Set-Based Estimation Using Zonotopes
- 2023
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In: 2023 European Control Conference, ECC 2023. - : Institute of Electrical and Electronics Engineers (IEEE).
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Conference paper (peer-reviewed)abstract
- For large-scale cyber-physical systems, the collaboration of spatially distributed sensors is often needed to perform the state estimation process. Privacy concerns naturally arise from disclosing sensitive measurement signals to a cloud estimator that predicts the system state. To solve this issue, we propose a differentially private set-based estimation protocol that preserves the privacy of the measurement signals. Compared to existing research, our approach achieves less privacy loss and utility loss using a numerically optimized truncated noise distribution. The proposed estimator is perturbed by weaker noise than the analytical approaches in the literature to guarantee the same level of privacy, therefore improving the estimation utility. Numerical and comparison experiments with truncated Laplace noise are presented to support our approach. Zonotopes, a less conservative form of set representation, are used to represent estimation sets, giving set operations a computational advantage. The privacy-preserving noise anonymizes the centers of these estimated zonotopes, concealing the precise positions of the estimated zonotopes.
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| 2. |
- Li, Zishuo, et al.
(author)
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Secure State Estimation against Sparse Attacks on a Time-varying Set of Sensors
- 2023
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In: IFAC-PapersOnLine. - : Elsevier BV. ; , s. 270-275
-
Conference paper (peer-reviewed)abstract
- This paper studies the problem of secure state estimation of a linear time-invariant (LTI) system with bounded noise in the presence of sparse attacks on an unknown, time-varying set of sensors. At each time, the attacker has the freedom to choose an arbitrary set of no more than p sensors and manipulate their measurements without restraint. To this end, we propose a secure state estimation scheme and guarantee a bounded estimation error irrespective of the attack signals subject to 2p-sparse observability and a mild, technical assumption that the system matrix has no degenerate eigenvalues. The proposed scheme comprises a design of decentralized observers for each sensor based on the local observable subspace decomposition. At each time step, the local estimates of sensors are fused by a median operator to obtain a secure estimation, which is then followed by a local detection-and-resetting process of the decentralized observers. The estimation error is shown to be upper-bounded by a constant which is determined only by the system parameters and noise magnitudes. Moreover, we design the detector threshold to ensure that the benign sensors never trigger the detector. The efficacy of the proposed algorithm is demonstrated by its application on a benchmark example of IEEE 14-bus system. We show that our proposed scheme can effectively tolerate sparse attacks on an unknown set of sensors, ensuring a bounded estimation error and effectively detecting and resetting the attacked sensors.
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| 3. |
- Liu, Changxin, et al.
(author)
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Event-Triggered Distributed Nonconvex Optimization with Progress-Based Threshold
- 2023
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In: 2023 62nd IEEE Conference on Decision and Control, CDC 2023. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 309-314
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Conference paper (peer-reviewed)abstract
- This work studies the distributed nonconvex optimization problem in bandwidth-limited communication environments. We develop a communication-efficient algorithm based on the gradient-tracking based distributed optimization method, where each computation node is equipped with a new event-triggered communication scheduler. Such scheduler approves the broadcasting only when the innovation of exchanged variables exceeds the change of decision variables in two consecutive updates. Compared to the conventional scheduler with time-dependent vanishing thresholds, the proposed one adapts better to the optimization dynamics and thus leads to more significant communication reduction. Finally, we prove the convergence of the algorithm and illustrate its performance via numerical examples.
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| 4. |
- Liu, Changxin, et al.
(author)
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Rate analysis of dual averaging for nonconvex distributed optimization
- 2023
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In: IFAC-PapersOnLine. - : Elsevier BV. ; , s. 5209-5214
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Conference paper (peer-reviewed)abstract
- This work studies nonconvex distributed constrained optimization over stochastic communication networks. We revisit the distributed dual averaging algorithm, which is known to converge for convex problems. We start from the centralized case, for which the change of two consecutive updates is taken as the suboptimality measure. We validate the use of such a measure by showing that it is closely related to stationarity. This equips us with a handle to study the convergence of dual averaging in nonconvex optimization. We prove that the squared norm of this suboptimality measure converges at rate O(1/t). Then, for the distributed setup we show convergence to the stationary point at rate O(1/t). Finally, a numerical example is given to illustrate our theoretical results.
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| 5. |
- Wang, Yuan, et al.
(author)
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Resilient distributed optimization under mobile malicious attacks
- 2023
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Conference paper (peer-reviewed)abstract
- This article addresses the distributed optimization problem in the presence of malicious adversaries that can move within the network and induce faulty behaviors in the attacked nodes. We first investigate the vulnerabilities of a consensus-based secure distributed optimization protocol under mobile adversaries. Then, a modified resilient distributed optimization algorithm is proposed. We develop conditions on the network structure for both complete and non-complete directed graph cases, under which the proposed algorithm guarantees that the estimates by regular nodes converge to the convex combination of the minimizers of their local functions. Simulations are carried out to verify the effectiveness of our approach.
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| 6. |
- Wu, Xuyang, et al.
(author)
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Delay-agnostic Asynchronous Coordinate Update Algorithm
- 2023
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In: ICML'23. ; , s. 37582-37606
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Conference paper (peer-reviewed)abstract
- We propose a delay-agnostic asynchronous coordinate update algorithm (DEGAS) for computing operator fixed points, with applications to asynchronous optimization. DEGAS includes novel asynchronous variants of ADMM and block-coordinate descent as special cases. We prove that DEGAS converges with both bounded and unbounded delays under delay-free parameter conditions. We also validate by theory and experiments that DEGAS adapts well to the actual delays. The effectiveness of DEGAS is demonstrated by numerical experiments on classification problems.
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| 7. |
- Wu, Xuyang, et al.
(author)
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Delay-agnostic Asynchronous Distributed Optimization
- 2023
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In: 2023 62Nd Ieee Conference On Decision And Control, Cdc. - : Institute of Electrical and Electronics Engineers (IEEE). - 9798350301243 ; , s. 1082-1087
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Conference paper (peer-reviewed)abstract
- Existing asynchronous distributed optimization algorithms often use diminishing step-sizes that cause slow practical convergence, or fixed step-sizes that depend on an assumed upper bound of delays. Not only is such a delay bound hard to obtain in advance, but it is also large and therefore results in unnecessarily slow convergence. This paper develops asynchronous versions of two distributed algorithms, DGD and DGD-ATC, for solving consensus optimization problems over undirected networks. In contrast to alternatives, our algorithms can converge to the fixed point set of their synchronous counterparts using step-sizes that are independent of the delays. We establish convergence guarantees under both partial and total asynchrony. The practical performance of our algorithms is demonstrated by numerical experiments.
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