Distributed Control Multi-Agent Systems

Latest papers

• SPADE-DFL: Communication-Efficient Decentralized Federated Learning via Derivative-Free Linearized ADMM

  Mengli Wei, Mengkai Zhu, Jiawen Chen, Wenwu Yu, Duxin Che · 25 September 2026

  Reducing communication in derivative-free decentralized learning requires controlling the disagreement accumulated over multiple local updates. This paper develops SPADE-DFL, a primal--dual method that allows the number of local function-value updates between neighbor exchanges to grow with the comp…

• ZO-COSMO: Index-Free One-Hop Mixing for Decentralized Zeroth-Order Optimization

  Shengjun Zhang, Tingyi Liu, Heng Zhang, Dong Xie · 24 September 2026

  Sparse communication in decentralized zeroth-order learning requires compatible peer-state coordinates. We characterize this one-hop condition and develop \textsf{ZO-COSMO}, coupling two-query estimation with average-preserving masked consensus using $q$ values per active link. Global supports serve…

• Fully Byzantine-Resilient Multi-Agent Reinforcement Learning

  Haejoon Lee, Dimitra Panagou · 23 September 2026

  We study distributed Byzantine-resilient actor-critic multi-agent reinforcement learning (AC-MARL), where agents collectively learn policies through local interactions. Existing methods guarantee convergence of the agents' parameters only to a neighborhood of the attack-free limit points, resulting …

• Convergence Theory of Knowledge Distillation in Asynchronous P2P Gossip Learning Network

  Lucas Qingyang Fang, Tiyao Liu, Jinhao Jing, Zeji Li, Kaijie Chen, Harikrishna Kuttivelil, Katia Obraczka · 3 September 2026

  Decentralized, serverless learning increasingly connects devices running different architectures, where the standard tool, decentralized SGD, is undefined as models with different parameter counts cannot be averaged. Knowledge distillation (KD) exchanges soft predictions rather than weights and side…

• Decentralized Federated Learning for Heterogeneous Multi-Task Semantic Communication

  Lin Yin, Tiejun Lv, Weicai Li, Xi Yu, Xiaoyu He · 18 August 2026

  Collaborative training in distributed semantic communication (DSC) networks typically relies on decentralized federated learning (DFL). However, pushing topology-agnostic aggregation into heterogeneous, multi-task environments creates a fundamental bottleneck: it drives negative transfer and overcon…

• Distributed Optimization with Streaming Data: A Temporal Weighting Perspective

  Muhammad Faraz Ul Abrar, Nicol\`o Michelusi, Erik G. Larsson · 11 August 2026

  Optimization theory is a widely used tool for intelligent decision-making. While classical optimization deals with fixed, time-invariant objective functions, many modern applications operate in dynamic environments where data arrive sequentially, and the learning objective evolves over time, often u…

• A Spectral Filtering Approach to Regret Analysis of Distributed Online Control for Linear Dynamical Systems

  Ting-Jui Chang · 4 August 2026

  This paper studies the distributed online control problem over a network of linear time-invariant (LTI) systems in the presence of adversarial disturbances and time-varying convex costs. The network cost is characterized by the summation of local cost functions, where each local function is sequenti…

• Using Non-Lipschitz Signum-based Functions for Distributed Optimization and Machine Learning: Trade-off Between Con-vergence Rate and Optimality Gap

  Mohammadreza Doostmohammadian, Amir Ahmad Ghods, Alireza Aghasi, Zulfiya R. Gabidullina, Hamid R. Rabiee · 4 August 2026

  In recent years, the prevalence of large-scale data-sets and the demand for sophisti-cated learning models have necessitated the development of efficient distributed ma-chine learning (ML) solutions. Convergence speed is a critical factor influencing the practicality and effectiveness of these distr…

• The Optimization Trilemma: Efficiency, Comfort and Fairness in Decentralized Multi-agent Coordination

  Jovan Nikolic, Maciej Krzysztof Zuziak, Evangelos Pournaras · 21 July 2026

  The problem of fair multi-agent coordination in decentralized settings is one of the most pressing challenges for building efficient collaborative systems. Resource allocation is based on optimized collective arrangements accounting for agents' needs. Such coordination should not only be computation…

• Decentralized Gradient Descent: Bottleneck Regimes and Budget Complexity

  Nicol\`o Michelusi · 15 July 2026

  Decentralized gradient descent (DGD) is widely used for solving distributed optimization problems over networks of agents. While its convergence properties are well understood, less is known about the communication and computation resources required to attain a prescribed accuracy. In this paper, we…

• Douglas-Rachford Splitting for Group-Sparse Feedback Linear-Quadratic Control

  Lechen Feng, Xun Li, Yuan-Hua Ni · 10 July 2026

  In this paper, we study the distributed linear quadratic problem with fixed communication topology (DFT-LQ) and the sparse feedback linear quadratic (SF-LQ) problem through a unified optimization framework. Specifically, both problems are formulated as a nonconvex, nonsmooth optimization problem equ…

• Decentralized Stochastic Subgradient-type Methods with Communication Compression for Nonsmooth Nonconvex Optimization

  Siyuan Zhang, Nachuan Xiao, Xin Liu · 3 July 2026

  In this paper, we consider the nonsmooth nonconvex decentralized optimization problem, where inter-agent communication is compressed. We propose a general framework that unifies various decentralized stochastic subgradient-type methods with unbiased compression and contractive compression with error…

• Robust Autonomous UAV Landing on Maritime Platforms via Multimodal Agentic AI and Active Wave Compensation

  Francisco S. Neves, Pedro N. Pereira, Raul D. S. G. Campilho, Andry M. Pinto · 1 July 2026

  Autonomous aerial inspection of marine infrastructure is frequently compromised by stochastic sea states, introducing risks of high-kinetic impacts, post-landing toppling, and sensory occlusion. This paper proposes a decoupled, multi-vehicle landing framework synchronizing an Unmanned Surface Vehicl…

• On the Necessity of a Liquid Substrate for Mesh Intelligence

  Hongwei Xu · 30 June 2026

  A mesh of sovereign agents has no center: no shared clock, no shared model, and no coordinator to gather data or retrain. Its competence rests on each agent folding the projections its peers emit into a single internal state, online, from observations that arrive at irregular, unscheduled times, on …

• How Task Structure Limits Multi-Agent Success: An Information-Theoretic Analysis

  Shi Pan, Ming Luo · 15 June 2026

  Multi-agent systems (MAS) were expected to overcome the limitation of single-agent systems (SAS) through collaboration. However, under typicality conditions on the task's constraint graph and bounded inter-agent communication, we prove that the success probability of a MAS is closely tied to the con…

• Decentralized Stochastic Nonconvex Optimization under the $(L_0,L_1)$-Smoothness

  Luo Luo, Xue Cui, Tingkai Jia, Cheng Chen · 3 June 2026

  This paper focuses on the decentralized stochastic optimization problem $f(\mathbf{x})=\frac{1}{m}\sum_{i=1}^m f_i(\mathbf{x})$ over a connected network of $n$ agents, where each local function has the form of $f_i(\mathbf{x}) = {\mathbb E}\left[F(\mathbf{x};{\boldsymbol \xi}_i)\right]$ which satisf…

• Network Distributed Multi-Agent Reinforcement Learning for Consensus Control of Quadcopters

  Youssef Mahran, Zeyad Gamal, Aamir Ahmad, Ayman El-Badawy · 2 June 2026

  This paper proposes a Network Distributed Multi-Agent Reinforcement Learning (ND-MARL) framework for quadcopter consensus control. Compared to conventional multi-agent MARL formulations that rely on centralized planning or fully decentralized execution, ND-MARL incorporates the swarm communication g…

• A Tight Theory of Error Feedback Algorithms in Distributed Optimization

  Daniel Berg Thomsen, Adrien Taylor, Aymeric Dieuleveut · 1 June 2026

  Communication costs are a major bottleneck in distributed learning and first-order optimization. A common approach to alleviate this issue is to compress the gradient information exchanged between agents. However, such compression typically degrades the convergence guarantees of gradient-based metho…

• Core-Halo Decomposition: Decentralizing Large-Scale Fixed-Point Problems

  Haixiang, Yang Xu, Jiefu Zhang, Xudong Wu, Zihan Zhou, Jun He, Jiayu Chen · 12 May 2026

  We study solving large-scale fixed-point equation \(x^\star=\bar F(x^\star)\) with decomposition. Standard strict decomposition assigns each agent a disjoint block and evaluates updates using only owned coordinates. For most operators, however, a block update may depend on variables outside the bloc…

• Distributed Associative Memory via Online Convex Optimization

  Bowen Wang, Matteo Zecchin, Osvaldo Simeone · 24 April 2026

  An associative memory (AM) enables cue-response recall, and associative memorization has recently been noted to underlie the operation of modern neural architectures such as Transformers. This work addresses a distributed setting where agents maintain a local AM to recall their own associations as w…

• A Generalized Sinkhorn Algorithm for Mean-Field Schr\"odinger Bridge

  Asmaa Eldesoukey, Yongxin Chen, Abhishek Halder · 9 April 2026

  The mean-field Schr\"odinger bridge (MFSB) problem concerns designing a minimum-effort controller that guides a diffusion process with nonlocal interaction to reach a given distribution from another by a fixed deadline. Unlike the standard Schr\"odinger bridge, the dynamical constraint for MFSB is t…

• Convergence of Byzantine-Resilient Gradient Tracking via Probabilistic Edge Dropout

  Amirhossein Dezhboro, Fateme Maleki, Arman Adibi, Erfan Amini, Jose E. Ramirez-Marquez · 2 April 2026

  We study distributed optimization over networks with Byzantine agents that may send arbitrary adversarial messages. We propose \emph{Gradient Tracking with Probabilistic Edge Dropout} (GT-PD), a stochastic gradient tracking method that preserves the convergence properties of gradient tracking under …

• Collaborative AI Agents and Critics for Fault Detection and Cause Analysis in Network Telemetry

  Syed Eqbal Alam, Zhan Shu · 2 April 2026

  We develop algorithms for collaborative control of AI agents and critics in a multi-actor, multi-critic federated multi-agent system. Each AI agent and critic has access to classical machine learning or generative AI foundation models. The AI agents and critics collaborate with a central server to c…

• Birch SGD: A Tree Graph Framework for Local and Asynchronous SGD Methods

  Alexander Tyurin, Danil Sivtsov · 31 March 2026

  We propose a new unifying framework, Birch SGD, for analyzing and designing distributed SGD methods. The central idea is to represent each method as a weighted directed tree, referred to as a computation tree. Leveraging this representation, we introduce a general theoretical result that reduces con…

• Distributed Online Submodular Maximization under Communication Delays: A Simultaneous Decision-Making Approach

  Zirui Xu, Vasileios Tzoumas · 31 March 2026

  We provide a distributed online algorithm for multi-agent submodular maximization under communication delays. We are motivated by the future distributed information-gathering tasks in unknown and dynamic environments, where utility functions naturally exhibit the diminishing-returns property, i.e., …