Budget-Bounded Incentives for Federated Learning

Richardson, Adam; Filos-Ratsikas, Aris; Faltings, Boi

doi:10.1007/978-3-030-63076-8_13

Cited by 20 publications

(15 citation statements)

References 8 publications

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“…This line of research inspires several data valuation methods using the Shapley value (Ghorbani, Kim, and Zou 2020;Ghorbani and Zou 2019;Jia et al 2020;Wang et al 2020), the core (Yan and Procaccia 2021), influence functions (Richardson, Filos-Ratsikas, and Faltings 2020b), and volume (Xu et al 2021b). Previous works have used concepts from mechanism design to elicit truthful reporting (Chen et al 2020;Richardson, Filos-Ratsikas, and Faltings 2020a) and to incentivize sharing data and/or model parameters in federated learning (Cong et al 2020;Kang et al 2019a,b;Lyu et al 2020;Yu et al 2020;Zhan et al 2020;Xu et al 2021a). Other works have addressed data privacy (Ding et al 2021;Hu et al 2019), adversarial robustness (Hayes and Ohrimenko 2018;So, Guler, and Avestimehr 2020), communication efficiency (Ding et al 2021), and fairness in Bayesian optimization (Sim et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Incentivizing Collaboration in Machine Learning via Synthetic Data Rewards

Tay¹,

Xu²,

Foo³

et al. 2021

Preprint

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This paper presents a novel collaborative generative modeling (CGM) framework that incentivizes collaboration among self-interested parties to contribute data to a pool for training a generative model (e.g., GAN), from which synthetic data are drawn and distributed to the parties as rewards commensurate to their contributions. Distributing synthetic data as rewards (instead of trained models or money) offers taskand model-agnostic benefits for downstream learning tasks and is less likely to violate data privacy regulation. To realize the framework, we firstly propose a data valuation function using maximum mean discrepancy (MMD) that values data based on its quantity and quality in terms of its closeness to the true data distribution and provide theoretical results guiding the kernel choice in our MMD-based data valuation function. Then, we formulate the reward scheme as a linear optimization problem that when solved, guarantees certain incentives such as fairness in the CGM framework. We devise a weighted sampling algorithm for generating synthetic data to be distributed to each party as reward such that the value of its data and the synthetic data combined matches its assigned reward value by the reward scheme. We empirically show using simulated and real-world datasets that the parties' synthetic data rewards are commensurate to their contributions.

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Section: Introductionmentioning

confidence: 99%

Incentivizing Collaboration in Machine Learning via Synthetic Data Rewards

Tay¹,

Xu²,

Foo³

et al. 2021

Preprint

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“…This data redundancy creates an opportunity for EDs to trick the MO and earn more rewards, i.e., EDs are enticed to free-riding attacks. To solve this problem, Richardson et al [105] designed a mechanism based on an influence metric that guarantees truthful reporting is the best strategy for EDs. Moreover, they also provide a bound on the incentive budget, i.e., the budget must be in proportion to the value of the FL model.…”

Section: B Analysis Of Fl Incentive Schemes From the Security Perspectivementioning

confidence: 99%

Incentive-Driven Federated Learning and Associated Security Challenges: A Systematic Review

Ali¹,

Ilahi²,

Qayyum³

et al. 2021

Preprint

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In response to various privacy risks, researchers and practitioners have been exploring different paradigms that can leverage the increased computational capabilities of consumer devices to train machine (ML) learning models in a distributed fashion without requiring the uploading of the training data from individual devices to central facilities. For this purpose, federated learning (FL) was proposed as a technique that can learn a global machine model at a central master node by the aggregation of models trained locally using private data. However, organizations may be reluctant to train models locally and to share these local ML models due to required computational resources for model training at their end and due to privacy risks that may result from adversaries inverting these models to infer information about the private training data. Incentive mechanisms have been proposed to motivate end users to participate in collaborative training of ML models (using their local data) in return for certain rewards. However, the design of an optimal incentive mechanism for FL is challenging due to its distributed nature and the fact that the central server has no access to clients’ hyperparameters information and the amount/quality data used for training, which makes the task of determining the reward based on the contribution of individual clients in FL environment difficult. Even though several incentive mechanisms have been proposed for FL, a thorough up-to-date systematic review is missing and this paper fills this gap. According to the best of our knowledge, this paper is the first systematic review that comprehensively enlists the design principles required for implementing these incentive mechanisms and then categorizes various incentive mechanisms according to their design principles. In addition, we also provide a comprehensive overview of security challenges associated with incentive-driven FL. Finally, we highlight the limitations and pitfalls of these incentive schemes and elaborate upon open-research issues that required further research attention.

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“…There exist a number of proposals for contribution measurement, i.e., algorithms that determine the quality of the service provided by the clients, for Federated Learning [16,1,28,33,23,31,36,29,37]. In particular, previous work established that Shapley Value, which measure the marginal loss caused by a client's sequential absence from the training, offer accurate contribution measurements.…”

Section: Introductionmentioning

confidence: 99%

“…More generally, the theoretical analysis reveals that clients with a skewed data distribution or a high data amount are not treated fairly by SVB. approach: all clients send all their model updates to the federator who in turn aggregates and computes the contribution via the marginal loss [28,33,23,31,36,29,37]. The main drawbacks of local approaches are the excessive communication overhead and the lower privacy due to directly exchanged model updates [1].…”

Section: Introductionmentioning

confidence: 99%

“…In contrast, the global approach has lower communication overhead and avoids the privacy leakage to other clients by communicating only with the federator. Prevailing examples of globally measuring contribution are Influence [28,29] and Shapley Value [33,37,36,30]. The prior art demonstrates that Shapley Value can effectively measure the client's contribution for the case when clients' data is i.i.d.…”

Section: Introductionmentioning

confidence: 99%

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Is Shapley Value fair? Improving Client Selection for Mavericks in Federated Learning

Huang¹,

Hong²,

Chen³

et al. 2021

Preprint

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Shapley Value is commonly adopted to measure and incentivize client participation in federated learning. In this paper, we show -theoretically and through simulations-that Shapley Value underestimates the contribution of a common type of client: the Maverick. Mavericks are clients that differ both in data distribution and data quantity and can be the sole owners of certain types of data. Selecting the right clients at the right moment is important for federated learning to reduce convergence times and improve accuracy. We propose FEDEMD, an adaptive client selection strategy based on the Wasserstein distance between the local and global data distributions. As FEDEMD adapts the selection probability such that Mavericks are preferably selected when the model benefits from improvement on rare classes, it consistently ensures the fast convergence in the presence of different types of Mavericks. Compared to existing strategies, including Shapley Value-based ones, FEDEMD improves the convergence of neural network classifiers by at least 26.9% for FedAvg aggregation compared with the state of the art.Preprint. Under review.

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Budget-Bounded Incentives for Federated Learning

Cited by 20 publications

References 8 publications

Incentivizing Collaboration in Machine Learning via Synthetic Data Rewards

Incentivizing Collaboration in Machine Learning via Synthetic Data Rewards

Incentive-Driven Federated Learning and Associated Security Challenges: A Systematic Review

Is Shapley Value fair? Improving Client Selection for Mavericks in Federated Learning

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