Incentive Design and Differential Privacy Based Federated Learning: A Mechanism Design Perspective

Kim, Sung-Wook

doi:10.1109/access.2020.3030888

Cited by 29 publications

(17 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To reduce the training rate of ML model on big data from Industrial IoT and to reduce the model aggregation's communication cost, Zhang et al [44] proposed a framework based on FL assisted by deep reinforcement learning. In a similar work, Kim Sungwoork [45] proposed an incentive mechanism for attracting several owners of data for joining in the process of FL to preserve privacy and efficient management of data. The proposed approach adopts two concepts: mechanism design for designing incentives to achieve the objectives, and differential privacy for privacy preservation.…”

Section: B Federated Learning Big Data Storagementioning

confidence: 99%

Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directions

Gadekallu,

Pham,

Huynh-The

et al. 2021

Preprint

View full text Add to dashboard Cite

Big data has remarkably evolved over the last few years to realize an enormous volume of data generated from newly emerging services and applications and a massive number of Internet-of-Things (IoT) devices. The potential of big data can be realized via analytic and learning techniques, in which the data from various sources is transferred to a central cloud for central storage, processing, and training. However, this conventional approach faces critical issues in terms of data privacy as the data may include sensitive data such as personal information, governments, banking accounts. To overcome this challenge, federated learning (FL) appeared to be a promising learning technique. However, a gap exists in the literature that a comprehensive survey on FL for big data services and applications is yet to be conducted. In this article, we present a survey on the use of FL for big data services and applications, aiming to provide general readers with an overview of FL, big data, and the motivations behind the use of FL for big data. In particular, we extensively review the use of FL for key big data services, including big data acquisition, big data storage, big data analytics, and big data privacy preservation. Subsequently, we review the potential of FL for big data applications, such as smart city, smart healthcare, smart transportation, smart grid, and social media. Further, we summarize a number of important projects on FL-big data and discuss key challenges of this interesting topic along with several promising solutions and directions.

show abstract

Section: B Federated Learning Big Data Storagementioning

confidence: 99%

Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directions

Gadekallu,

Pham,

Huynh-The

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…differential [47], model-poisoning [85], white-box inference [86] attacks, etc.). The latter are often combined with innovative techniques or emerging technologies, such as differential privacy [87], homomorphic encryption [88], blockchain [89], etc. However, explicitly modeling and integrating human user feedback information, through the creation of user profiles Q l,m and their incorporation in the FL mechanism (Section V-C), inevitably requires the elaboration and extension of the aforementioned methodologies.…”

Section: Conclusion and Future Research Directionsmentioning

confidence: 99%

Towards Open and Expandable Cognitive AI Architectures for Large-Scale Multi-Agent Human-Robot Collaborative Learning

2021

View full text Add to dashboard Cite

Learning from Demonstration (LfD) constitutes one of the most robust methodologies for constructing efficient cognitive robotic systems. Despite the large body of research works already reported, current key technological challenges include those of multi-agent learning and long-term autonomy. Towards this direction, a novel cognitive architecture for multi-agent LfD robotic learning is introduced in this paper, targeting to enable the reliable deployment of open, scalable and expandable robotic systems in large-scale and complex environments. In particular, the designed architecture capitalizes on the recent advances in the Artificial Intelligence (AI) (and especially the Deep Learning (DL)) field, by establishing a Federated Learning (FL)-based framework for incarnating a multi-human multi-robot collaborative learning environment. The fundamental conceptualization relies on employing multiple AI-empowered cognitive processes (implementing various robotic tasks) that operate at the edge nodes of a network of robotic platforms, while global AI models (underpinning the aforementioned robotic tasks) are collectively created and shared among the network, by elegantly combining information from a large number of human-robot interaction instances. Regarding pivotal novelties, the designed cognitive architecture a) introduces a new FL-based formalism that extends the conventional LfD learning paradigm to support large-scale multi-agent operational settings, b) elaborates previous FL-based self-learning robotic schemes so as to incorporate the human in the learning loop and c) consolidates the fundamental principles of FL with additional sophisticated AI-enabled learning methodologies for modelling the multi-level inter-dependencies among the robotic tasks. The applicability of the proposed framework is explained using an example of a realworld industrial case study (subject to ongoing research activities) for agile production-based Critical Raw Materials (CRM) recovery. INDEX TERMSLearning from demonstration, human-robot interaction, artificial intelligence, federated learning VOLUME XX, XXXX This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.

show abstract

“…Similarly, Kim et al [74] also proposed the use of the VCG mechanism for an incentive scheme with the focus on guaranteeing users' privacy. The proposed system consists of a set of MOs and a set of EDs in which each MO is associated with some IoT devices and has its budget.…”

Section: Mechanism Design Based Mechanismsmentioning

confidence: 99%

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

Ali¹,

Ilahi²,

Qayyum³

et al. 2021

Preprint

View full text Add to dashboard Cite

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.

show abstract

Incentive Design and Differential Privacy Based Federated Learning: A Mechanism Design Perspective

Cited by 29 publications

References 16 publications

Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directions

Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directions

Towards Open and Expandable Cognitive AI Architectures for Large-Scale Multi-Agent Human-Robot Collaborative Learning

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

Contact Info

Product

Resources

About