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1. Resilient Distributed Optimization for Multiagent Cyberphysical Systems

   https://doi.org/10.1109/TAC.2025.3532791  

   Yemini, Michal; Nedić, Angelia; Goldsmith, Andrea J; Gil, Stephanie (June 2025, IEEE Transactions on Automatic Control)

   This work focuses on the problem of distributed optimization in multiagent cyberphysical systems, where a legitimate agent’s iterates are influenced both by the values it receives from potentially malicious neighboring agents and by its own self-serving target function. We develop a new algorithmic and analytical framework to achieve resilience for the class of problems where stochastic values of trust between agents exist and can be exploited. In this case, we show that convergence to the true global optimal point can be recovered, both in mean and almost surely, even in the presence of malicious agents. Furthermore, we provide expected convergence rate guarantees in the form of upper bounds on the expected squared distance to the optimal value. Finally, numerical results are presented that validate our analytical convergence guarantees even when the malicious agents compose the majority of agents in the network and where existing methods fail to converge to the optimal nominal points. 

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2. PrivOpt: an intrinsically private distributed optimization algorithm

   https://doi.org/10.23919/ACC53348.2022.9867222  

   Esteki, Amir-Salar; Kia, Solmaz S. (June 2022, 2022 American Control Conference (ACC))

   A critical factor for expanding the adoption of networked solutions is ensuring local data privacy of in-network agents implementing a distributed algorithm. In this paper, we consider privacy preservation in the distributed optimization problem in the sense that local cost parameters should not be revealed. Current approaches to privacy preservation normally propose methods that sacrifice exact convergence or increase communication overhead. We propose PrivOpt, an intrinsically private distributed optimization algorithm that converges exponentially fast without any convergence error or using extra communication channels. We show that when the number of the parameters of the local cost is greater than the dimension of the decision variable of the problem, no malicious agent, even if it has access to all transmitted-in and -out messages in the network, can obtain local cost parameters of other agents. As an application study, we show how our proposed PrivOpt algorithm can be used to solve an optimal resource allocation problem with the guarantees that the local cost parameters of all the agents stay private. 

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3. Distributed optimal resource allocation using transformed primal-dual method

   https://doi.org/10.23919/ACC55779.2023.10156601  

   Kia, Solmaz S.; Wei, Jingrong; Chen, Long (May 2023, American Control Conference)

   We consider an in-network optimal resource allocation problem in which a group of agents interacting over a connected graph want to meet a demand while minimizing their collective cost. The contribution of this paper is to design a distributed continuous-time algorithm for this problem inspired by a recently developed first-order transformed primal-dual method. The solution applies to cluster-based setting where each agent may have a set of subagents, and its local cost is the sum of the cost of these subagents. The proposed algorithm guarantees an exponential convergence for strongly convex costs and asymptotic convergence for convex costs. Exponential convergence when the local cost functions are strongly convex is achieved even when the local gradients are only locally Lipschitz. For convex local cost functions, our algorithm guarantees asymptotic convergence to a point in the minimizer set. Through numerical examples, we show that our proposed algorithm delivers a faster convergence compared to existing distributed resource allocation algorithms. 

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4. Distributed Detection of Adversarial Attacks for Resilient Cooperation of Multi-Robot Systems with Intermittent Communication

   Bahrami, Rayan; Jafarnejadsani, Hamidreza (October 2024, IEEE transactions on control of network systems)

   This paper concerns the consensus and formation of a network of mobile autonomous agents in adversarial settings where a group of malicious (compromised) agents are subject to deception attacks. In addition, the communication network is arbitrarily time-varying and subject to intermittent connections, possibly imposed by denial-of-service (DoS) attacks. We provide explicit bounds for network connectivity in an integral sense, enabling the characterization of the system’s resilience to specific classes of adversarial attacks. We also show that under the condition of connectivity in an integral sense uniformly in time, the system is finite-gain L stable and uniformly exponentially fast consensus and formation are achievable, provided malicious agents are detected and isolated from the network. We present a distributed and reconfigurable framework with theoretical guarantees for detecting malicious agents, allowing for the resilient cooperation of the remaining cooperative agents. Simulation studies are provided to illustrate the theoretical findings. 

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5. Learning Trust Over Directed Graphs in Multiagent Systems

   Akgun, Orhan Eren; Dayı, Arif Kerem; Gil, Stephanie; Nedic, Angelia (June 2023, Proceedings of Machine Learning Research)

   Matni, N.; Morari, M.; Pappas, G. J. (Ed.)

   We address the problem of learning the legitimacy of other agents in a multiagent network when an unknown subset is comprised of malicious actors. We specifically derive results for the case of directed graphs and where stochastic side information, or observations of trust, is available. We refer to this as “learning trust” since agents must identify which neighbors in the network are reliable, and we derive a learning protocol to achieve this. We also provide analytical results showing that under this protocol i) agents can learn the legitimacy of all other agents almost surely, and ii) the opinions of the agents converge in mean to the true legitimacy of all other agents in the network. Lastly, we provide numerical studies showing that our convergence results hold for various network topologies and variations in the number of malicious agents. 

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