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Register a new userA VCG-based Fair Incentive Mechanism for Federated Learning
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Added by
Mingshu Cong
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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Federated learning (FL) has shown great potential for addressing the challenge of isolated data islands while preserving data privacy. It allows artificial intelligence (AI) models to be trained on locally stored data in a distributed manner. In order to build an ecosystem for FL to operate in a sustainable manner, it has to be economically attractive to data owners. This gives rise to the problem of FL incentive mechanism design, which aims to find the optimal organizational and payment structure for the federation in order to achieve a series of economic objectives. 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 FL settings. It maximizes the social surplus and minimizes unfairness of the federation. We provide an implementation of FVCG with neural networks and theoretic proofs on its performance bounds. Extensive numerical experiment results demonstrated the effectiveness and economic reasonableness of FVCG.
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Federated learning (FL) serves as a data privacy-preserved machine learning paradigm, and realizes the collaborative model trained by distributed clients. To accomplish an FL task, the task publisher needs to pay financial incentives to the FL server and FL server offloads the task to the contributing FL clients. It is challenging to design proper incentives for the FL clients due to the fact that the task is privately trained by the clients. This paper aims to propose a contract theory based FL task training model towards minimizing incentive budget subject to clients being individually rational (IR) and incentive compatible (IC) in each FL training round. We design a two-dimensional contract model by formally defining two private types of clients, namely data quality and computation effort. To effectively aggregate the trained models, a contract-based aggregator is proposed. We analyze the feasible and optimal contract solutions to the proposed contract model. %Experimental results demonstrate that the proposed framework and contract model can effective improve the generation accuracy of FL tasks. Experimental results show that the generalization accuracy of the FL tasks can be improved by the proposed incentive mechanism where contract-based aggregation is applied.
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. The latter studies how to protect data owners privacy while maintaining high utility of trained models. However, incentive mechanism and privacy protection in FL have been studied separately and no work solves both problems at the same time. In this work, we address the two problems simultaneously by an FL-Market that incentivizes data owners participation by providing appropriate payments and privacy protection. FL-Market enables data owners to obtain compensation according to their privacy loss quantified by local differential privacy (LDP). Our insight is that, by meeting data owners personalized privacy preferences and providing appropriate payments, we can (1) incentivize privacy risk-tolerant data owners to set larger privacy parameters (i.e., gradients with less noise) and (2) provide preferred privacy protection for privacy risk-averse data owners. To achieve this, we design a personalized LDP-based FL framework with a deep learning-empowered auction mechanism for incentivizing trading gradients with less noise and optimal aggregation mechanisms for model updates. Our experiments verify the effectiveness of the proposed framework and mechanisms.
A distributed machine learning platform needs to recruit many heterogeneous worker nodes to finish computation simultaneously. As a result, the overall performance may be degraded due to straggling workers. By introducing redundancy into computation, coded machine learning can effectively improve the runtime performance by recovering the final computation result through the first $k$ (out of the total $n$) workers who finish computation. While existing studies focus on designing efficient coding schemes, the issue of designing proper incentives to encourage worker participation is still under-explored. This paper studies the platforms optimal incentive mechanism for motivating proper workers participation in coded machine learning, despite the incomplete information about heterogeneous workers computation performances and costs. A key contribution of this work is to summarize workers multi-dimensional heterogeneity as a one-dimensional metric, which guides the platforms efficient selection of workers under incomplete information with a linear computation complexity. Moreover, we prove that the optimal recovery threshold $k$ is linearly proportional to the participator number $n$ if we use the widely adopted MDS (Maximum Distance Separable) codes for data encoding. We also show that the platforms increased cost due to incomplete information disappears when worker number is sufficiently large, but it does not monotonically decrease in worker number.
Recent years have witnessed a rapid proliferation of smart Internet of Things (IoT) devices. IoT devices with intelligence require the use of effective machine learning paradigms. 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. 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 process. We present several open research challenges with their possible solutions. Finally, we provide an outlook on future research.
Crowd sensing is a new paradigm which leverages the ubiquity of sensor-equipped mobile devices to collect data. To achieve good quality for crowd sensing, incentive mechanisms are indispensable to attract more participants. Most of existing mechanisms focus on the expected utility prior to sensing, ignoring the risk of low quality solution and privacy leakage. Traditional incentive mechanisms such as the Vickrey-Clarke-Groves (VCG) mechanism and its variants are not applicable here. In this paper, to address these challenges, we propose a behavior based incentive mechanism for crowd sensing applications with budget constraints by applying sequential all-pay auctions in mobile social networks (MSNs), not only to consider the effects of extensive user participation, but also to maximize high quality of the context based sensing content submission for crowd sensing platform under the budget constraints, where users arrive in a sequential order. Through an extensive simulation, results indicate that incentive mechanisms in our proposed framework outperform the best existing solution.
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