Social Learning in Multi Agent Multi Armed Bandits

SankararamanAbishek,; GaneshAyalvadi,; Shakkottai, Sanjay

doi:10.1145/3366701

Cited by 34 publications

(7 citation statements)

References 47 publications

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“…Here, we define communication complexity as the total number of message exchanges, with the message consisting of arm index, observed reward, and possibly other information. In homogeneous settings, where all agents are identical in that K 𝑣 = K for all 𝑣 ∈ V, arm elimination-type algorithms [61] as well as gossip-type protocols [16,51] have been shown to be communication efficient and effective in terms of group regret.…”

Section: Goalsmentioning

confidence: 99%

“…A more realistic scenario in such systems is that agents form the nodes of an underlying communication network, where adjacent nodes can exchange messages but cannot reach more distant nodes in just one hop. This scenario has been investigated under various conditions, using simple communication protocols such as flooding ("message-passing") algorithms and gossiping to disseminate information [11,16,41,42,51,54,57].…”

Section: Introductionmentioning

confidence: 99%

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Communication-Efficient Collaborative Heterogeneous Bandits in Networks

Lee¹,

Schmid²,

Yun³

2023

Preprint

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The multi-agent multi-armed bandit problem has been studied extensively due to its ubiquity in many real-life applications, such as online recommendation systems and wireless networking. We consider the setting where agents should minimize their group regret while collaborating over a given graph via some communication protocol and where each agent is given a different set of arms. Previous literature on this problem only considered one of the two desired features separately: agents with the same arm set communicate over a general graph, or agents with different arm sets communicate over a fully connected graph. In this work, we introduce a more general problem setting that encompasses all the desired features. For this novel setting, we first provide a rigorous regret analysis for the standard flooding protocol combined with the UCB policy. Then, to mitigate the issue of high communication costs incurred by flooding, we propose a new protocol called Flooding with Absorption (FWA). We provide a theoretical analysis of the regret bound and intuitions on the advantages of using FWA over flooding. Lastly, we verify empirically that using FWA leads to significantly lower communication costs despite minimal regret performance loss compared to flooding. CCS CONCEPTS• Theory of computation → Multi-agent learning; Online learning theory; Sequential decision making; Regret bounds; Distributed algorithms; • Networks → Network protocol design.

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Section: Goalsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Communication-Efficient Collaborative Heterogeneous Bandits in Networks

Lee¹,

Schmid²,

Yun³

2023

Preprint

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show abstract

“…The work in [251] established the first logarithmic upper bound on the number of communication rounds needed for an optimal regret bound. The authors considered a complete graph network topology, wherein a set of agents are initialized with a disjoint set of arms.…”

Section: Via2 Distributed Bandits Formulationsmentioning

confidence: 99%

Pervasive AI for IoT Applications: Resource-efficient Distributed Artificial Intelligence

Baccour,

Mhaisen,

Abdellatif

et al. 2021

Preprint

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Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of Things (IoT) applications and services, spanning from recommendation systems and speech processing applications to robotics control and military surveillance. This is driven by the easier access to sensory data and the enormous scale of pervasive/ubiquitous devices that generate zettabytes (ZB) of real-time data streams. Designing accurate models using such data streams, to predict future insights and revolutionize the decision-taking process, inaugurates pervasive systems as a worthy paradigm for a better quality-oflife. The confluence of pervasive computing and artificial intelligence, Pervasive AI, expanded the role of ubiquitous IoT systems from mainly data collection to executing distributed computations with a promising alternative to centralized learning, presenting various challenges, including privacy concerns, scalability, and latency requirements. In this context, a wise cooperation and resource scheduling should be envisaged among IoT devices (e.g., smartphones, smart vehicles) and infrastructure (e.g. edge nodes, and base stations) to avoid communication and computation overheads and ensure maximum performance. In this paper, we conduct a comprehensive survey of the recent techniques and strategies developed to overcome these resource challenges in pervasive AI systems. Specifically, we first present an overview of the pervasive computing, its architecture, and its intersection with artificial intelligence. We then review the background, applications and performance metrics of AI, particularly Deep Learning (DL) and online learning, running in a ubiquitous system. Next, we provide a deep literature review of communication-efficient techniques, from both algorithmic and system perspectives, of distributed inference, training and online learning tasks across the combination of IoT devices, edge devices and cloud servers. Finally, we discuss our future vision and research challenges.

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“…The work in [142] established the first logarithmic upper bound on the number of communication rounds needed for an optimal regret bound. The authors considered a complete graph network topology, wherein a set of agents are initialized with a disjoint set of arms.…”

mentioning

confidence: 99%

Pervasive AI for IoT Applications: A Survey on Resource-Efficient Distributed Artificial Intelligence

Baccour

Mhaisen

Abdellatif

et al. 2022

IEEE Commun. Surv. Tutorials

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Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of Things (IoT) applications and services, spanning from recommendation systems and speech processing applications to robotics control and military surveillance. This is driven by the easier access to sensory data and the enormous scale of pervasive/ubiquitous devices that generate zettabytes of real-time data streams. Designing accurate models using such data streams, to revolutionize the decision-taking process, inaugurates pervasive computing as a worthy paradigm for a better quality-of-life (e.g., smart homes and self-driving cars.). The confluence of pervasive computing and artificial intelligence, namely Pervasive AI, expanded the role of ubiquitous IoT systems from mainly data collection to executing distributed computations with a promising alternative to centralized learning, presenting various challenges, including privacy and latency requirements. In this context, an intelligent resource scheduling should be envisaged among IoT devices (e.g., smartphones, smart vehicles) and infrastructure (e.g., edge nodes and base stations) to avoid communication and computation overheads and ensure maximum performance. In this paper, we conduct a comprehensive survey of the recent techniques and strategies developed to overcome these resource challenges in pervasive AI systems. Specifically, we first present an overview of the pervasive computing, its architecture, and its intersection with artificial intelligence. We then review the background, applications and performance metrics of AI, particularly Deep Learning (DL) and reinforcement learning, running in a ubiquitous system. Next, we provide a deep literature review of communication-efficient techniques, from both algorithmic and system perspectives, of distributed training and inference across the combination of IoT devices, edge devices and cloud servers. Finally, we discuss our future vision and research challenges.

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Social Learning in Multi Agent Multi Armed Bandits

Cited by 34 publications

References 47 publications

Communication-Efficient Collaborative Heterogeneous Bandits in Networks

Communication-Efficient Collaborative Heterogeneous Bandits in Networks

Pervasive AI for IoT Applications: Resource-efficient Distributed Artificial Intelligence

Pervasive AI for IoT Applications: A Survey on Resource-Efficient Distributed Artificial Intelligence

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