When Information Freshness Meets Service Latency in Federated Learning: A Task-Aware Incentive Scheme for Smart Industries

Lim, Wei Yang Bryan; Xiong, Zehui; Kang, Jiawen; Niyato, Dusit; Leung, Cyril; Chen, Miao

doi:10.1109/tii.2020.3046028

Cited by 55 publications

(22 citation statements)

References 26 publications

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“…where ω > 0 refers to the model owner preference for bandwidth contributed from the edge servers. Specifically, ω is larger if a model owner requires the learning task to be completed more urgently, e.g., when information freshness [52] and thus higher uplink rates are important for model training, or when more workers are required to train the model collaboratively, e.g., for location dependent tasks such as location-based recommender systems since each worker may only cover a small area of interest [53].…”

Section: A Problem Formulationmentioning

confidence: 99%

Dynamic Edge Association and Resource Allocation in Self-Organizing Hierarchical Federated Learning Networks

Lim

Xiong

et al. 2021

IEEE J. Select. Areas Commun.

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111

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Federated Learning (FL) is a promising privacypreserving distributed machine learning paradigm. However, communication inefficiency remains the key bottleneck that impedes its large-scale implementation. Recently, hierarchical FL (HFL) has been proposed in which data owners, i.e., workers, can first transmit their updated model parameters to edge servers for intermediate aggregation. This reduces the instances of global communication and straggling workers. To enable efficient HFL, it is important to address the issues of edge association and resource allocation in the context of non-cooperative players, i.e., workers, edge servers, and model owner. However, the existing studies merely focus on static approaches and do not consider the dynamic interactions and bounded rationalities of the players. In this paper, we propose a hierarchical game framework to study the dynamics of edge association and resource allocation in self-organizing HFL networks. In the lower-level game, the edge association strategies of the workers are modelled using an evolutionary game. In the upper-level game, a Stackelberg differential game is adopted in which the model owner decides an optimal reward scheme given the expected bandwidth allocation control strategy of the edge server. Finally, we provide numerical results to validate that our proposed framework captures the HFL system dynamics under varying sources of network heterogeneity.

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Section: A Problem Formulationmentioning

confidence: 99%

Dynamic Edge Association and Resource Allocation in Self-Organizing Hierarchical Federated Learning Networks

Lim

Xiong

et al. 2021

IEEE J. Select. Areas Commun.

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111

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“…Subsequently, there is a room to further explore the role of FL in MCDM methodologies and enhance the accuracy and reliability of the results. FL has a great potential to improve the capability of decision support functionalities, such as risk management, in logistics and SC management (Lim et al , 2020). Therefore, the synergy of FL and BWM can be further investigated in the area of CCRM.…”

Section: Literature Reviewmentioning

confidence: 99%

Risk quantification in cold chain management: a federated learning-enabled multi-criteria decision-making methodology

Lau

Tsang

Nakandala

et al. 2021

IMDS

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PurposeIn the cold supply chain (SC), effective risk management is regarded as an essential component to address the risky and uncertain SC environment in handling time- and temperature-sensitive products. However, existing multi-criteria decision-making (MCDM) approaches greatly rely on expert opinions for pairwise comparisons. Despite the fact that machine learning models can be customised to conduct pairwise comparisons, it is difficult for small and medium enterprises (SMEs) to intelligently measure the ratings between risk criteria without sufficiently large datasets. Therefore, this paper aims at developing an enterprise-wide solution to identify and assess cold chain risks.Design/methodology/approachA novel federated learning (FL)-enabled multi-criteria risk evaluation system (FMRES) is proposed, which integrates FL and the best–worst method (BWM) to measure firm-level cold chain risks under the suggested risk hierarchical structure. The factors of technologies and equipment, operations, external environment, and personnel and organisation are considered. Furthermore, a case analysis of an e-grocery SC in Australia is conducted to examine the feasibility of the proposed approach.FindingsThroughout this study, it is found that embedding the FL mechanism into the MCDM process is effective in acquiring knowledge of pairwise comparisons from experts. A trusted federation in a cold chain network is therefore formulated to identify and assess cold SC risks in a systematic manner.Originality/valueA novel hybridisation between horizontal FL and MCDM process is explored, which enhances the autonomy of the MCDM approaches to evaluate cold chain risks under the structured hierarchy.

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“…The authors in [134] proposed a contract-theoretic taskaware incentive mechanism to model the tradeoff between preferences for service latency and AoI of different training tasks. The problem of training resource trading for the MO having different preferences to the service latency and AoI is considered.…”

Section: A Incentive Mechanisms Based On Contract Theorymentioning

confidence: 99%

Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspective

Tu¹,

Luong²,

Niyato³

et al. 2021

Preprint

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Federated learning (FL) becomes popular and has shown great potentials in training large-scale machine learning (ML) models without exposing the owners' raw data. In FL, the data owners can train ML models based on their local data and only send the model updates rather than raw data to the model owner for aggregation. To improve learning performance in terms of model accuracy and training completion time, it is essential to recruit sufficient participants. Meanwhile, the data owners are rational and may be unwilling to participate in the collaborative learning process due to the resource consumption. To address the issues, there have been various works recently proposed to motivate the data owners to contribute their resources. In this paper, we provide a comprehensive review for the economic and game theoretic approaches proposed in the literature to design various schemes for incentivizing data owners to participate in FL training process. In particular, we first present the fundamentals and background of FL, economic theories commonly used in incentive mechanism design. Then, we review applications of game theory and economic approaches applied for incentive mechanisms design of FL. Finally, we highlight some open issues and future research directions concerning incentive mechanism design of FL.

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When Information Freshness Meets Service Latency in Federated Learning: A Task-Aware Incentive Scheme for Smart Industries

Cited by 55 publications

References 26 publications

Dynamic Edge Association and Resource Allocation in Self-Organizing Hierarchical Federated Learning Networks

Dynamic Edge Association and Resource Allocation in Self-Organizing Hierarchical Federated Learning Networks

Risk quantification in cold chain management: a federated learning-enabled multi-criteria decision-making methodology

Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspective

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