Competing Adaptive Networks

• URL: http://arxiv.org/abs/2103.15664v1
• Date: Mon, 29 Mar 2021 14:42:15 GMT
• Title: Competing Adaptive Networks
• Authors: Stefan Vlaski and Ali H. Sayed
• Abstract summary: 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.
• Score: 56.56653763124104
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Adaptive networks have the capability to pursue solutions of global stochastic optimization problems by relying only on local interactions within neighborhoods. The diffusion of information through repeated interactions allows for globally optimal behavior, without the need for central coordination. Most existing strategies are developed for cooperative learning settings, where the objective of the network is common to all agents. We consider in this work a team setting, where a subset of the agents form a team with a common goal while competing with the remainder of the network. We develop an algorithm for decentralized competition among teams of adaptive agents, analyze its dynamics and present an application in the decentralized training of generative adversarial neural networks.

Related papers

• Performance-Aware Self-Configurable Multi-Agent Networks: A Distributed Submodular Approach for Simultaneous Coordination and Network Design [3.5527561584422465]
  We present AlterNAting COordination and Network-Design Algorithm (Anaconda) Anaconda is a scalable algorithm that also enjoys near-optimality guarantees. We demonstrate in simulated scenarios of area monitoring and compare it with a state-of-the-art algorithm.
  arXiv  Detail & Related papers  (2024-09-02T18:11:33Z)
• Initialisation and Network Effects in Decentralised Federated Learning [1.5961625979922607]
  Decentralised federated learning enables collaborative training of individual machine learning models on a distributed network of communicating devices. This approach avoids central coordination, enhances data privacy and eliminates the risk of a single point of failure. We propose a strategy for uncoordinated initialisation of the artificial neural networks based on the distribution of eigenvector centralities of the underlying communication network.
  arXiv  Detail & Related papers  (2024-03-23T14:24:36Z)
• Decentralized and Lifelong-Adaptive Multi-Agent Collaborative Learning [57.652899266553035]
  Decentralized and lifelong-adaptive multi-agent collaborative learning aims to enhance collaboration among multiple agents without a central server. We propose DeLAMA, a decentralized multi-agent lifelong collaborative learning algorithm with dynamic collaboration graphs.
  arXiv  Detail & Related papers  (2024-03-11T09:21:11Z)
• Federated Multi-Level Optimization over Decentralized Networks [55.776919718214224]
  We study the problem of distributed multi-level optimization over a network, where agents can only communicate with their immediate neighbors. We propose a novel gossip-based distributed multi-level optimization algorithm that enables networked agents to solve optimization problems at different levels in a single timescale. Our algorithm achieves optimal sample complexity, scaling linearly with the network size, and demonstrates state-of-the-art performance on various applications.
  arXiv  Detail & Related papers  (2023-10-10T00:21:10Z)
• One More Step Towards Reality: Cooperative Bandits with Imperfect Communication [15.440053226788562]
  We study cooperative bandit learning under three typical real-world communication scenarios. We propose decentralized algorithms that achieve competitive performance. We present an improved delayed-update algorithm that outperforms the existing state-of-the-art on various network topologies.
  arXiv  Detail & Related papers  (2021-11-24T13:19:11Z)
• Learning Autonomy in Management of Wireless Random Networks [102.02142856863563]
  This paper presents a machine learning strategy that tackles a distributed optimization task in a wireless network with an arbitrary number of randomly interconnected nodes. We develop a flexible deep neural network formalism termed distributed message-passing neural network (DMPNN) with forward and backward computations independent of the network topology.
  arXiv  Detail & Related papers  (2021-06-15T09:03:28Z)
• 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)
• Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions [80.49176924360499]
  We establish a framework for directing a society of simple, specialized, self-interested agents to solve sequential decision problems. We derive a class of decentralized reinforcement learning algorithms. We demonstrate the potential advantages of a society's inherent modular structure for more efficient transfer learning.
  arXiv  Detail & Related papers  (2020-07-05T16:41:09Z)
• Collective Learning by Ensembles of Altruistic Diversifying Neural Networks [0.0]
  We present a model for co-learning by ensembles of interacting neural networks that aim to maximize their own performance but also their functional relations to other networks. We show that ensembles of interacting networks outperform independent ones, and that optimal ensemble performance is reached when the coupling between networks increases diversity and degrades the performance of individual networks.
  arXiv  Detail & Related papers  (2020-06-20T22:53:32Z)
• Multi-Agent Reinforcement Learning for Adaptive User Association in Dynamic mmWave Networks [17.295158818748188]
  We propose a scalable and flexible algorithm for user association based on multi-agent reinforcement learning. Users act as independent agents that, based on their local observations only, learn to autonomously coordinate their actions in order to optimize the network sum-rate. Simulation results show that the proposed algorithm is able to adapt to (fast) changes of radio environment, thus providing large sum-rate gain in comparison to state-of-the-art solutions.
  arXiv  Detail & Related papers  (2020-06-16T10:51:27Z)

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.