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One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning
[article]
2021
arXiv
pre-print
Preserved Fulltext
In recent years, federated learning has been embraced as an approach for bringing about collaboration across large populations of learning agents. ...
Furthermore, we compare the sample complexity of incentive-aware collaboration with that of optimal collaboration when one ignores agents' incentives. ...
In particular, we show that there is a factor Ω( √ k) gap between the socially optimal sample complexity and that of optimal stable or envy-free equilibria for k agents. ...
arXiv:2103.03228v1
fatcat:g5q2ionfqndw7ctmivwitgewie
Incentivizing Data Contribution in Cross-Silo Federated Learning
[article]
2022
arXiv
pre-print
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In cross-silo federated learning, clients (e.g., organizations) collectively train a global model using their local data. ...
We consider two types of equilibrium structures: symmetric and asymmetric equilibria. We show that the three mechanisms admit an identical symmetric equilibrium structure. ...
INTRODUCTION Federated learning (FL) is a decentralized machine learning paradigm where multiple clients collaboratively train a global model under the orchestration of a central server [1] . ...
arXiv:2203.03885v1
fatcat:4pwskfk6drespbgplymfz2rzoq
Modern Applied Science, Vol. 3, No. 1, January 2009, all in one file
2008
Modern Applied Science
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Yamin, Universiti Kebangsaan Malaysia for helping in doing microanalysis; as well as Desy Anggraini, Chandra Rini P. and Arif Dwi R. who help us in the preparation of the samples. ...
Acknowledgments The authors would like to thank to The Directorate of Research and Services, Directorate General of Higher Education, The Ministry of Cultural and Education of Republic of Indonesia that ...
Land use is one of the essential tools for nearly all land development. ...
doi:10.5539/mas.v3n1p0
fatcat:jovqztw2qre4ra46ggzarwfatu
Stackelberg Security Game For Optimizing Security Of Federated Internet Of Things Platform Instances
2017
Zenodo
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This paper presents an approach for optimal cyber security decisions to protect instances of a federated Internet of Things (IoT) platform in the cloud. ...
Naïve Q-Learning belongs to the category of active and model-free Machine Learning (ML) techniques in which the agent (either the defender or the attacker) attempts to find an optimal security solution ...
Fig. 1 1 NIMBLE federated collaboration network with sector-centric platform instances
Stackelberg Security Game for Optimizing Security of Federated Internet of Things Platform Instances International ...
doi:10.5281/zenodo.1130143
fatcat:ioupac6cbvaz3lftjcptnz5aru
A Flat World, a Level Playing Field, a Small World After All, or None of the Above? A Review of Thomas L. Friedman'sThe World is Flat
2007
Journal of Economic Literature
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Of course, standardization, mechanization, and computerization all work to increase the number of footloose tasks, but innovation and education work in the opposite direction, creating relationship-based ...
In fact, most of the footloose relationship-free jobs in apparel and footwear and consumer electronics departed the United States several decades ago, and few U.S. workers today feel the force of Chinese ...
trade, 6 percent in the federal government, 6 percent in hospitals, 5 percent in ambulatory care and so on. ...
doi:10.1257/jel.45.1.83
fatcat:ckjoalvntrajfchsfuybzmfdw4
More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence
[article]
2020
arXiv
pre-print
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With a focus on regular machine learning, distributed machine learning, deep learning, and multi-agent systems, the purpose of this article is to deliver a new view on many possibilities for improving ...
It can also be used to improve security, stabilize learning, build fair models, and impose composition in selected areas of AI. ...
Differential privacy in federated learning 1) Overview of federated learning: Federated learning enables individual users to collaboratively learn a shared prediction model while keeping all the training ...
arXiv:2008.01916v1
fatcat:ujmxv7eq6jcppndfu5shbzkdom
More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence
2020
IEEE Transactions on Knowledge and Data Engineering
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With a focus on regular machine learning, distributed machine learning, deep learning, and multi-agent systems, the purpose of this article is to deliver a new view on many possibilities for improving ...
It can also be used to improve security, stabilize learning, build fair models, and impose composition in selected areas of AI. ...
Fig. 5 . 5 Differential privacy in deep learning
Federated learning enables individual users to collaboratively learn a shared prediction model while keeping all the
TABLE 1 1 Properties of differential ...
doi:10.1109/tkde.2020.3014246
fatcat:33rl6jxy5rgexpnuel5rvlkg5a
Collaboration in Participant-Centric Federated Learning: A Game-Theoretical Perspective
[article]
2022
arXiv
pre-print
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Federated learning (FL) is a promising distributed framework for collaborative artificial intelligence model training while protecting user privacy. ...
A bootstrapping component that has attracted significant research attention is the design of incentive mechanism to stimulate user collaboration in FL. ...
Summarizing concept and applications in federated learning, Yang et al. classify federated learning into three types: horizontal federated learning, vertical federated learning and federated transfer learning ...
arXiv:2207.12030v1
fatcat:6r4task245drzbj5ssknmrfna4
California's Sacramento–San Joaquin Delta Conflict: From Cooperation to Chicken
2012
Journal of water resources planning and management
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This paper traces changes in this conflict in game-theoretic terms, with its implications for the region's physical and ecological decline and governance. ...
The State of California may become the victim (or chicken) of the Delta game, bearing the greatest costs, if it continues to rely on leaving parties to develop voluntary cooperative solutions without a ...
When playing repeated games, players change their strategies over time and adopt a mixture of strategies on the basis of learning about the behavioral characteristics of the other players in the game, ...
doi:10.1061/(asce)wr.1943-5452.0000164
fatcat:77m7jenkyjg65ddudqc6djo5ze
Tamer Başar [People in Control]
2021
IEEE Control Systems
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), and how does all this affect equilibria? ...
These all involve the dependence of equilibria on the kind of information players acquire during the decision-making process, who communicates with whom, and how actions of a particular player affect the ...
doi:10.1109/mcs.2021.3107761
fatcat:hivhw75dtvfkneirsqqhpluw4a
Innovation and Strategic Network Formation
[article]
2022
arXiv
pre-print
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Their decisions determine interaction rates between firms, and these interaction rates enter our model as link probabilities in a learning network. ...
We study a model of innovation with a large number of firms that create new technologies by combining several discrete ideas. These ideas are created via private investment and spread between firms. ...
We will show that there exist levels of openness q such that for all i the choice q i is optimal given p and q −i . ...
arXiv:1911.06872v4
fatcat:f2czdknbufcgjms2kqhug2qr3e
Optimality and Stability in Federated Learning: A Game-theoretic Approach
[article]
2021
arXiv
pre-print
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One branch of this research has taken a game-theoretic approach, and in particular, prior work has viewed federated learning as a hedonic game, where error-minimizing players arrange themselves into federating ...
In this work, we motivate and define a notion of optimality given by the average error rates among federating agents (players). ...
Avrim Blum, Nika Haghtalab, Richard Lanas Phillips, and Han Shao. One for one, or all for all: Equilibria and optimality of collaboration in federated learning. arXiv preprint arXiv:2103.03228, 2021. ...
arXiv:2106.09580v1
fatcat:fcnrs62tbfbivo6jceyzdby66m
Competing for congestible goods: experimental evidence on parking choice
2020
Scientific Reports
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Our results give insights on how to deal with parking problems such as the design of parking lots in central locations in cities and open the way to better understand similar congestible goods problems ...
We study experimentally a congestible goods problem of relevance for parking design, namely how people choose between a convenient parking lot with few spots and a less convenient one with unlimited space ...
Clearly, the optimal decision requires coordination and collaboration among the players and is not feasible in the uncoordinated environment we consider here. ...
doi:10.1038/s41598-020-77711-w
pmid:33257701
fatcat:pmregw6l7zf27a3axztzareh5m
Data Pricing in Machine Learning Pipelines
[article]
2021
arXiv
pre-print
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We also investigate pricing in the step of collaborative training of machine learning models, and overview pricing machine learning models for end users in the step of machine learning deployment. ...
Then, we focus on pricing in three important steps in machine learning pipelines. To understand pricing in the step of training data collection, we review pricing raw data sets and data labels. ...
To succeed in building a machine learning application, one party is far from enough. Many parties have to collaborate in one way or another. ...
arXiv:2108.07915v1
fatcat:736zip2pbndupl7hixdbfz33om
Privacy-Preserving Federated Learning for UAV-Enabled Networks: Learning-Based Joint Scheduling and Resource Management
[article]
2020
arXiv
pre-print
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However, due to the privacy concerns of devices and limited computation or communication resource of UAVs, it is impractical to send raw data of devices to UAV servers for model training. ...
In this paper, we develop an asynchronous federated learning (AFL) framework for multi-UAV-enabled networks, which can provide asynchronous distributed computing by enabling model training locally without ...
[19] proposed an FL-based sensing and collaborative learning approach for UAV-enabled internet of vehicles (IoVs), where UAVs collect data and train AI model for IoVs. In addition, Ng et al. ...
arXiv:2011.14197v1
fatcat:jrwqdrdfkbh3joqa2nichlclxu
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