Networked Signal and Information Processing

• URL: http://arxiv.org/abs/2210.13767v2
• Date: Tue, 18 Apr 2023 15:22:44 GMT
• Title: Networked Signal and Information Processing
• Authors: Stefan Vlaski, Soummya Kar, Ali H. Sayed, Jos\'e M. F. Moura
• Abstract summary: The article reviews significant advances in networked signal and information processing, which have enabled in the last 25 years extending decision making and inference, optimization, control, and learning to the increasingly ubiquitous environments of distributed agents. networked agents, through cooperation and sharing, are able to match the performance of cloud or federated solutions, while offering the potential for improved privacy, increasing resilience, and saving resources.
• Score: 56.572301493342174
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The article reviews significant advances in networked signal and information processing, which have enabled in the last 25 years extending decision making and inference, optimization, control, and learning to the increasingly ubiquitous environments of distributed agents. As these interacting agents cooperate, new collective behaviors emerge from local decisions and actions. Moreover, and significantly, theory and applications show that networked agents, through cooperation and sharing, are able to match the performance of cloud or federated solutions, while offering the potential for improved privacy, increasing resilience, and saving resources.

Related papers

• Communication Learning in Multi-Agent Systems from Graph Modeling Perspective [62.13508281188895]
  We introduce a novel approach wherein we conceptualize the communication architecture among agents as a learnable graph. We introduce a temporal gating mechanism for each agent, enabling dynamic decisions on whether to receive shared information at a given time.
  arXiv  Detail & Related papers  (2024-11-01T05:56:51Z)
• LLM-Based Cooperative Agents using Information Relevance and Plan Validation [5.299803738642663]
  Multi-agent cooperation involves interacting with a 3D scene and cooperating with decentralized agents under complex partial observations. Current systems demonstrate inefficiency in managing acquired information through observation. The failure to incorporate spatial data into decision-making processes restricts the agent's ability to construct optimized trajectories. We propose the RElevance and Validation-Enhanced Cooperative Language Agent (REVECA), a novel cognitive architecture powered by GPT-3.5.
  arXiv  Detail & Related papers  (2024-05-27T01:47:14Z)
• Operational Collective Intelligence of Humans and Machines [7.8074313693407635]
  We explore the use of aggregative crowdsourced forecasting (ACF) as a mechanism to help operationalize collective intelligence'' This research asks whether ACF, as a key way to enable Operational Collective Intelligence, could be brought to bear on operational scenarios.
  arXiv  Detail & Related papers  (2024-02-16T22:45:09Z)
• Collaborative Information Dissemination with Graph-based Multi-Agent Reinforcement Learning [2.9904113489777826]
  This paper introduces a Multi-Agent Reinforcement Learning (MARL) approach for efficient information dissemination. We propose a Partially Observable Game (POSG) for information dissemination empowering each agent to decide on message forwarding independently. Our experimental results show that our trained policies outperform existing methods.
  arXiv  Detail & Related papers  (2023-08-25T21:30:16Z)
• Attention Based Feature Fusion For Multi-Agent Collaborative Perception [4.120288148198388]
  We propose an intermediate collaborative perception solution in the form of a graph attention network (GAT) The proposed approach develops an attention-based aggregation strategy to fuse intermediate representations exchanged among multiple connected agents. This approach adaptively highlights important regions in the intermediate feature maps at both the channel and spatial levels, resulting in improved object detection precision.
  arXiv  Detail & Related papers  (2023-05-03T12:06:11Z)
• Compressed Regression over Adaptive Networks [58.79251288443156]
  We derive the performance achievable by a network of distributed agents that solve, adaptively and in the presence of communication constraints, a regression problem. We devise an optimized allocation strategy where the parameters necessary for the optimization can be learned online by the agents.
  arXiv  Detail & Related papers  (2023-04-07T13:41:08Z)
• Privacy-Preserving Joint Edge Association and Power Optimization for the Internet of Vehicles via Federated Multi-Agent Reinforcement Learning [74.53077322713548]
  We investigate the privacy-preserving joint edge association and power allocation problem. The proposed solution strikes a compelling trade-off, while preserving a higher privacy level than the state-of-the-art solutions.
  arXiv  Detail & Related papers  (2023-01-26T10:09:23Z)
• Competing Adaptive Networks [56.56653763124104]
  We develop an algorithm for decentralized competition among teams of adaptive agents. We present an application in the decentralized training of generative adversarial neural networks.
  arXiv  Detail & Related papers  (2021-03-29T14:42:15Z)
• Learning Connectivity for Data Distribution in Robot Teams [96.39864514115136]
  We propose a task-agnostic, decentralized, low-latency method for data distribution in ad-hoc networks using Graph Neural Networks (GNN) Our approach enables multi-agent algorithms based on global state information to function by ensuring it is available at each robot. We train the distributed GNN communication policies via reinforcement learning using the average Age of Information as the reward function and show that it improves training stability compared to task-specific reward functions.
  arXiv  Detail & Related papers  (2021-03-08T21:48:55Z)
• Resource Management in Wireless Networks via Multi-Agent Deep Reinforcement Learning [15.091308167639815]
  We propose a mechanism for distributed resource management and interference mitigation in wireless networks using multi-agent deep reinforcement learning (RL) We equip each transmitter in the network with a deep RL agent that receives delayed observations from its associated users, while also exchanging observations with its neighboring agents. Our proposed framework enables agents to make decisions simultaneously and in a distributed manner, unaware of the concurrent decisions of other agents.
  arXiv  Detail & Related papers  (2020-02-14T19:01:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.