Federated Learning as a Network Effects Game.

We are the first to model clients' behaviors in FL as a network effects game, where each client's benefit depends on other clients who also join the network. ... Federated Learning (FL) aims to foster collaboration among a population of clients to improve the accuracy of machine learning without directly sharing local data. ... Morgan Faculty Award, a Facebook Research Award, and a Mozilla Research Grant. ...

arXiv:2302.08533v1 fatcat:mgabqgfrwbdrdogeiycbjwwqnu

We model the incentive-based interaction between a global server and participating devices for federated learning via a Stackelberg game to motivate the participation of the devices in the federated learning ... Federated learning can be a promising solution for enabling IoT-based smart applications. In this paper, we present the primary design aspects for enabling federated learning at network edge. ... FEDERATED LEARNING AT THE EDGE: KEY DESIGN ASPECTS A. ...

arXiv:1911.05642v3 fatcat:k55v2icilnblpcunxougmfjw54

Multiple Versions

Federated Learning is an emerging distributed collaborative learning paradigm adopted by many of today's applications, e.g., keyboard prediction and object recognition. ... Its core principle is to learn from large amount of users data while preserving data privacy by design as collaborative users only need to share the machine learning models and keep data locally. ... Thus, contract theory is a type of Unbalanced Stackelberg game, with the federator as the leader and dominant the optimization objective of the federated learning system. ...

doi:10.1109/tps-isa50397.2020.00014 fatcat:ey4zo4htwfcybg5euc2axtkqtq

Federated Learning is an emerging distributed collaborative learning paradigm adopted by many of today's applications, e.g., keyboard prediction and object recognition. ... Its core principle is to learn from large amount of users data while preserving data privacy by design as collaborative users only need to share the machine learning models and keep data locally. ... Thus, contract theory is a type of Unbalanced Stackelberg game, with the federator as the leader and dominant the optimization objective of the federated learning system. ...

arXiv:2011.06830v1 fatcat:hn5utzq2nvh5tlmmroiw6wqb7y

Open Access

Federated ecology involves many fields such as machine learning algorithms, distributed machine learning, cryptography and security, privacy-preserving data mining, game theory, and so on. ... They propose a long-term memory cell on actions and game context to learn the hidden representations. ...

doi:10.1109/tcss.2021.3063801 fatcat:6ya7ghhqircjbmm4ihc5i34vwm

This paper presents an approach for optimal cyber security decisions to protect instances of a federated Internet of Things (IoT) platform in the cloud. ... We augment the repeated SSG (including SHARP and SUQR) with a reinforced learning algorithm called Naïve Q-Learning. ... Such a game is formulated as a Stackelberg Game and uses linear programming to solve the identified problem. ...

doi:10.5281/zenodo.1130143 fatcat:ioupac6cbvaz3lftjcptnz5aru

Open Access

Considering users' heterogeneous training samples and data distributions, we formulate the optimal social cluster formation problem as a federation game and devise a fair revenue allocation mechanism to ... Experiments on Facebook network and MNIST/CIFAR-10 datasets validate that our SCFL can effectively enhance learning utility, improve user payoff, and enforce customizable privacy protection. ... Federation Game Formulation In federation game, the mutual communication capability among all players is a basic assumption, which the social network in our work can enable. ...

arXiv:2212.13992v2 fatcat:zitdkyg63vhknpacrjvpnh3h6y

Multiple Versions

To strengthen data privacy and security, federated learning as an emerging machine learning technique is proposed to enable large-scale nodes, e.g., mobile devices, to distributedly train and globally ... in federated learning. ... Computation Model for Federated Learning We consider a federated learning task as a monopoly market with a monopolist operator (a task publisher) and a set of mobile devices N = {1, . . . , N }. ...

arXiv:1905.07479v2 fatcat:jhyv7ks4gfes5i4ngdpb6f22uy

Multiple Versions

As a distributed learning approach, federated learning trains a shared learning model over distributed datasets while preserving the training data privacy. ... We formulate the interactions among the PLOs and vehicles as a multi-lead multi-follower Stackelberg game. ... Federated Learning for Vehicular Networks As a privacy-preserving learning approach, federated learning enables the collaborative training of a globally shared learning model without exchanging raw data ...

doi:10.1109/tvt.2021.3098170 fatcat:bdw2nh52h5hvziji4kyzn5svyu

Multiple Versions

The meaningful participation of stakeholders in decision-making is now widely recognized as a crucial element of effective water resource management, particularly with regards to adapting to climate and ... Social learning has been identified as particularly important in transboundary contexts, where it is necessary to reframe problems from a local to a basin-wide perspective. ... Serious games may therefore be seen as a form of intervention within a multi-stakeholder network setting that involves learning and changing of stakeholders' mental frames of transboundary water issues ...

doi:10.3390/w8050175 fatcat:xurmli5dfnaklbx24dinqjyduq

DOAJ Szczepanski

As an emerging secure learning paradigm in leveraging cross-agency private data, vertical federated learning (VFL) is expected to improve advertising models by enabling the joint learning of complementary ... Empirical studies on three industrial datasets exhibit the effectiveness of our methods, with the median AUC over all datasets improved by 0.86% and 2.6% in the local deployment mode and the federated ... We use α as a hyper-parameter to balance the effect of raw label and the federated soft label. ...

arXiv:2205.15987v3 fatcat:hknahx6qffeinjm7lwiblvvc4e

Open Access Multiple Versions

Federated learning (FL) becomes popular and has shown great potentials in training large-scale machine learning (ML) models without exposing the owners' raw data. ... In this paper, we provide a comprehensive review for the economic and game theoretic approaches proposed in the literature to design various schemes for stimulating data owners to participate in FL training ... INCENTIVE MECHANISM DESIGN FOR FEDERATED LEARNING A. ...

arXiv:2111.11850v1 fatcat:24xqnqiqtbh2hdn6lnkdkctqii

In the article "Hierarchical incentive mechanism design for federated machine learning in mobile networks," Lim et al. proposed a federated learning-based privacypreserving approach to facilitate collaborative ... and energy efficiency, as well as to mitigate the network traffic burdens. ...

doi:10.1109/jiot.2020.3019948 fatcat:mogalqnhnnaqpbxb7zivzdhvry

Our result shows that the learning structure can effectively solve the cooperation problem in a large scale network with decent scalability. ... Reinforcement learning (RL) is known as model-free and high efficient intelligent algorithm for communication problems and proved useful in the communication network. ... We highlighted that federated learning can effectively accelerate the convergence speed and enhance cooperation. We investigated the theoretical basis for the benefit of federated learning. ...

arXiv:2108.00506v3 fatcat:7f7lu25bjvc3jjjiviutdws2am

Multiple Versions

Federated Learning (FL) is a rapidly growing field in machine learning that allows data to be trained across multiple decentralized devices. ... We also cover performance-aware selections and as well as resource-aware selections for resource-constrained networks and heterogeneous networks. ... The authors in [40] propose a novel approach to developing an incentive mechanism for Federated Learning (FL) using game theory by using the framework of a Stackelberg game in which FL users are the ...

arXiv:2311.06801v1 fatcat:z6w3wzegvza3posfhjvs4fhfui

Open Access

Federated Learning as a Network Effects Game [article]

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Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism [article]

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An Exploratory Analysis on Users' Contributions in Federated Learning

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An Exploratory Analysis on Users' Contributions in Federated Learning [article]

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Federated Ecology: Steps Toward Confederated Intelligence

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Stackelberg Security Game For Optimizing Security Of Federated Internet Of Things Platform Instances

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Social-Aware Clustered Federated Learning with Customized Privacy Preservation [article]

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Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach [article]

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FedParking: A Federated Learning based Parking Space Estimation with Parked Vehicle assisted Edge Computing

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Exploring the Potential Impact of Serious Games on Social Learning and Stakeholder Collaborations for Transboundary Watershed Management of the St. Lawrence River Basin

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Achieving Lightweight Federated Advertising with Self-Supervised Split Distillation [article]

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Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspective [article]

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Special Issue on Artificial-Intelligence-Powered Edge Computing for Internet of Things

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Scalable Multi-agent Reinforcement Learning Algorithm for Wireless Networks [article]

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A Comprehensive Survey On Client Selections in Federated Learning [article]

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