Latency Optimization for Blockchain-Empowered Federated Learning in Multi-Server Edge Computing

Nguyen, Dinh C.; Hosseinalipour, Seyyedali; Love, David J.; Pathirana, Pubudu N.; Brinton, Christopher G.

doi:10.48550/arxiv.2203.09670

Cited by 4 publications

(7 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The orthogonal frequency division multiple access (OFDMA) technique is adopted in wireless B-FL system. All devices and edge servers are assumed to have a single antenna for simplification [14], [39], [44], which can be generalized to the multi-antenna case by additionally allocating the power for each antenna of each server. We assume that the channel state information(CSI) is available for resource allocation and static in one time-slot but vary from different time-slots.…”

Section: Communication Modelmentioning

confidence: 99%

Trustworthy Federated Learning via Blockchain

Yang

Shi

Zhou

et al. 2023

IEEE Internet Things J.

View full text Add to dashboard Cite

The safety-critical scenarios of artificial intelligence (AI), such as autonomous driving, Internet of Things, smart healthcare, etc., have raised critical requirements of trustworthy AI to guarantee the privacy and security with reliable decisions. As a nascent branch for trustworthy AI, federated learning (FL) has been regarded as a promising privacy preserving framework for training a global AI model over collaborative devices. However, security challenges still exist in the FL framework, e.g., Byzantine attacks from malicious devices, and model tampering attacks from malicious server, which will degrade or destroy the accuracy of trained global AI model. In this paper, we shall propose a decentralized blockchain based FL (B-FL) architecture by using a secure global aggregation algorithm to resist malicious devices, and deploying practical Byzantine fault tolerance consensus protocol with high effectiveness and low energy consumption among multiple edge servers to prevent model tampering from the malicious server. However, to implement B-FL system at the network edge, multiple rounds of cross-validation in blockchain consensus protocol will induce long training latency. We thus formulate a network optimization problem that jointly considers bandwidth and power allocation for the minimization of longterm average training latency consisting of progressive learning rounds. We further propose to transform the network optimization problem as a Markov decision process and leverage the deep reinforcement learning based algorithm to provide high system performance with low computational complexity. Simulation results demonstrate that B-FL can resist malicious attacks from edge devices and servers, and the training latency of B-FL can be significantly reduced by deep reinforcement learning based algorithm compared with baseline algorithms.

show abstract

Section: Communication Modelmentioning

confidence: 99%

Trustworthy Federated Learning via Blockchain

Yang

Shi

Zhou

et al. 2023

IEEE Internet Things J.

View full text Add to dashboard Cite

show abstract

“…simulation results show that the scheme improves throughput and reduces energy consumption while ensuring privacy security. Nguyen et al [147] proposed an architecture, BFL, and a series of solutions which include offloading strategies, ML model aggregation and a new DRL approach. Simulation results show that these measures outperformed existing methods in terms of training efficiency, convergence speed and latency.…”

Section: ) Integration Needsmentioning

confidence: 99%

A Survey of State-of-the-art on Edge Computing: Theoretical Models, Technologies, Directions, and Development Paths

Liu

Luo

Chen³

et al. 2022

IEEE Access

View full text Add to dashboard Cite

In order to describe the roadmap of current edge computing research activities, we first gave a brief overview of the most advanced edge computing surveys published in the last six years. We found that edge computing has been adaptively integrated into growing number of applications. Edge computing theory and technology will bring substantial innovation and incentive, as well as a large number of application scenarios in different fields, such as edge computing assisted smart city, Internet of Vehicles(IoV), Industrial Internet and many other different fields. However, we believe that a comprehensive investigation of the essence of edge computing is still missing by using the most advanced theoretical models, technologies, directions and development paths. To fill this gap, we have conducted an in-depth survey by identifying and classifying the latest high-quality research results related to theoretical discoveries in edge computing, as well as edge convergence technologies that intersect with the frontiers of Information and Communication Technology (ICT). Finally, we also summarize several promising open issues as future research directions. We hope that this survey report will attract much more attention, stimulate fruitful discussions, and provide ideas and useful guidance for further research on the theoretical models, technologies, directions and development paths of edge computing.

show abstract

“…Furthermore, applying blockchain on HFL can lead to extra latency during broadcasting, verification, and consensus to generate a new block. Although there is some research that optimizes the latency of BHFL by designing resource allocation mechanisms among devices [17], it still cannot resolve the influence of blockchain consensus on latency.…”

Section: Introductionmentioning

confidence: 99%

Incentive Mechanism Design for Joint Resource Allocation in Blockchain-Based Federated Learning

Wang

et al. 2023

IEEE Trans. Parallel Distrib. Syst.

View full text Add to dashboard Cite

Cloud-edge-device hierarchical federated learning (HFL) has been recently proposed to achieve communication-efficient and privacy-preserving distributed learning. However, there exist several critical challenges, such as the single point of failure and potential stragglers in both edge servers and local devices. To resolve these issues, we propose a decentralized and straggler-tolerant blockchain-based HFL (BHFL) framework. Specifically, a Raft-based consortium blockchain is deployed on edge servers to provide a distributed and trusted computing environment for global model aggregation in BHFL. To mitigate the influence of stragglers on learning, we propose a novel aggregation method, HieAvg, which utilizes the historical weights of stragglers to estimate the missing submissions. Furthermore, we optimize the overall latency of BHFL by jointly considering the constraints of global model convergence and blockchain consensus delay. Theoretical analysis and experimental evaluation show that our proposed BHFL based on HieAvg can converge in the presence of stragglers, which performs better than the traditional methods even when the loss function is non-convex and the data on local devices are non-independent and identically distributed (non-IID).

show abstract

Latency Optimization for Blockchain-Empowered Federated Learning in Multi-Server Edge Computing

Cited by 4 publications

References 34 publications

Trustworthy Federated Learning via Blockchain

Trustworthy Federated Learning via Blockchain

A Survey of State-of-the-art on Edge Computing: Theoretical Models, Technologies, Directions, and Development Paths

Incentive Mechanism Design for Joint Resource Allocation in Blockchain-Based Federated Learning

Contact Info

Product

Resources

About