Incentive mechanism in federated learning

Author: zuhl

August undefined, 2024

WebDec 1, 2024 · Zeng [28] design the incentive mechanism with a novel multi-dimensional perspective for federated learning. In [36] , [37] , Ding et al. use the contract-theoretic approach to design an optimal incentive mechanism for the parameter server, which considers clients’ multi-dimensional private information, e.g., training overhead and ... WebJan 1, 2024 · Request PDF Incentive Mechanism Design for Federated Learning In federated learning, motivating data owners to continue participating in a data federation …

Incentive Mechanism Design for Federated Learning: …

WebApr 9, 2024 · However, the challenges such as incentive mechanisms for participating in training and worker (i.e., mobile devices) selection schemes for reliable federated learning, have not been explored yet. WebJan 19, 2024 · The current research on the incentive mechanism of FL lacks the accurate assessment of clients’ truthfulness and reliability, and the incentive mechanism based on untruthful and unreliable... how to say beach ball in spanish

Incentivizing Differentially Private Federated Learning: A Multi ...

WebNov 24, 2024 · The incentive mechanism for federated learning to motivate edge nodes to contribute model training is studied and a deep reinforcement learning-based (DRL) incentive mechanism has been designed to determine the optimal pricing strategy for the parameter server and the optimal training strategies for edge nodes. 192 Highly Influential … WebJun 8, 2024 · Federated learning (FL) is an emerging paradigm for machine learning, in which data owners can collaboratively train a model by sharing gradients instead of their raw data. Two fundamental research problems in FL are incentive mechanism and privacy protection. The former focuses on how to incentivize data owners to participate in FL. WebEnsuring fairness in incentive mechanisms for federated learning (FL) is essential to attracting high-quality clients and building a sustainable FL ecosystem. Most existing fairness-aware incentive mechanisms distribute rewards to FL clients by quantifying their contributions to the performance of the global model. Essentially, these mechanisms … north florida shelling beaches

Incentive Mechanisms for Federated Learning

Design of Two-Level Incentive Mechanisms for …

WebOct 13, 2024 · We presented the FL incentive mechanism, B-LSP, based on the Generalized Second Price Auction (GSP). This mechanism can overcome the issue of unmanageable incentives while calculating the reward values. Furthermore, a magnitude stratification is introduced to ensure the participants remain active and the basic need for data volume in … WebUSENIX The Advanced Computing Systems Association how to say beach in italianWebMay 1, 2024 · In this work, we propose FGFL, a novel incentive governor for Federated Learning to conduct efficient Federated Learning in the highly heterogeneous and dynamic scenarios. Specifically, FGFL contains two main parts: 1) a fair incentive mechanism and 2) a reliable incentive management system. how to say beach in japanese

"WebNov 26, 2024 · The system is, to the best of our knowledge, the first game for studying participants’ reactions under various incentive mechanisms under federated learning scenarios. Data collected can be used to analyse behaviour patterns exhibited by human players, and inform future FL incentive mechanism design research. " - Incentive mechanism in federated learning

Incentive mechanism in federated learning

A Survey of Incentive Mechanism Design for Federated …

WebIn order to effectively solve these problems, we propose FIFL, a fair incentive mechanism for federated learning. FIFL rewards workers fairly to attract reliable and efficient ones while punishing and eliminating the malicious ones based on a dynamic real-time worker assessment mechanism. Web[10] Zhan Y, Zhang J, Hong Z, et al. A survey of incentive mechanism design for federated learning[J]. IEEE Transactions on Emerging Topics in Computing, 2024. ... Zeng R, Zeng C, …

Did you know?

WebIn this federated learning program, we select and reward participants by combining the reputation and bids of the participants under a limited budget. Theoretical analysis proves … WebJan 1, 2024 · Cross-silo federated learning (FL) is a privacypreserving distributed machine learning where organizations acting as clients cooperatively train a global model without …

WebApr 9, 2024 · However, the challenges such as incentive mechanisms for participating in training and worker (i.e., mobile devices) selection schemes for reliable federated … WebEnsuring fairness in incentive mechanisms for federated learning (FL) is essential to attracting high-quality clients and building a sustainable FL ecosystem. Most existing …

WebAug 9, 2024 · In this chapter, we have proposed two incentive mechanisms, such as Stackelberg game-based incentive mechanism and the auction theory-based incentive … WebAug 15, 2024 · In this paper, we present a VCG-based FL incentive mechanism, named FVCG, specifically designed for incentivizing data owners to contribute all their data and truthfully report their costs in...

WebAs the initial variant of federated learning (FL), horizontal federated learning (HFL) applies to the situations where datasets share the same feature space but differ in the sample …

WebIn order to effectively solve these problems, we propose FIFL, a fair incentive mechanism for federated learning. FIFL rewards workers fairly to attract reliable and efficient ones while … north florida shipyard jacksonvilleWebMar 7, 2024 · Blockchain-based federated learning (BCFL) has recently gained tremendous attention because of its advantages, such as decentralization and privacy protection of raw data. However, there has been few studies focusing on the allocation of resources for the participated devices (i.e., clients) in the BCFL system. Especially, in the BCFL framework … north florida shipyards accidentWebApr 9, 2024 · Hierarchical Federated Learning (HFL) is a distributed machine learning paradigm tailored for multi-tiered computation architectures, which supports massive access of devices' models simultaneously. To enable efficient HFL, it is crucial to design suitable incentive mechanisms to ensure that devices actively participate in local training. … north florida shipyard addressWebJan 20, 2024 · A Learning-Based Incentive Mechanism for Federated Learning Abstract: Internet of Things (IoT) generates large amounts of data at the network edge. Machine … how to say beach in hawaiianWeb[10] Zhan Y, Zhang J, Hong Z, et al. A survey of incentive mechanism design for federated learning[J]. IEEE Transactions on Emerging Topics in Computing, 2024. ... Zeng R, Zeng C, Wang X, et al. A comprehensive survey of incentive mechanism for federated learning[J]. arXiv preprint arXiv:2106.15406, 2024. [12] Huang J, Kong L, Chen G, et al ... north florida shipyard jobsWebNov 26, 2024 · This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated learning model against different types of attacks such as data leakage and/or data poisoning. Secondly, the book presents incentive mechanisms which aim to encourage individuals to participate in the federated learning ... north florida slaughterhouseWebApr 20, 2024 · Federated learning is a new distributed machine learning paradigm that many clients (e.g., mobile devices or organizations) collaboratively train a model under the … north florida sleep resources