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A review of federated learning in renewable energy applications:Potential,challenges,and future directions

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Energy and AI

Energy and AI 2024年第3期17卷 444-457页

作者： Albin Grataloup Stefan Jonas Angela Meyer Bern University of Applied Sciences School of Engineering and Computer ScienceQuellgasse 21Biel2501Switzerland Universitàdella Svizzera italiana Faculty of InformaticsVia la Santa 1Lugano-Viganello6962Switzerland Delft University of Technology Department of Geoscience and Remote SensingStevinweg 1Delft2628The Netherlands

Federated learning has recently emerged as a privacy-preserving distributed machine learning *** learning enables collaborative training of multiple clients and entire fleets without sharing the involved training *** preserving data privacy,federated learning has the potential to overcome the lack of data sharing in the renewable energy sector which is inhibiting innovation,research and *** paper provides an overview of federated learning in renewable energy *** discuss federated learning algorithms and survey their applications and case studies in renewable energy generation and *** also evaluate the potential and the challenges associated with federated learning applied in power and energy ***,we outline promising future research directions in federated learning for applications in renewable energy.

关键词： Federated learning Renewable energy Dis tributed learning data heterogeneity Privacy data sharing Wind energy Solar energy Buildings Review

Topology design and graph embedding for decentralized federated learning

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Intelligent and Converged Networks 2024年第2期5卷 100-115页

作者： Yubin Duan Xiuqi Li Jie Wu Department of Computer and Information Sciences Temple UniversityPhiladelphiaPA 19122USA

Federated learning has been widely employed in many applications to protect the data privacy of participating *** the dataset is decentralized among training devices in federated learning,the model parameters are usually stored in a centralized *** federated learning is easy to implement;however,a centralized scheme causes a communication bottleneck at the central server,which may significantly slow down the training *** improve training efficiency,we investigate the decentralized federated learning *** decentralized scheme has become feasible with the rapid development of device-to-device communication techniques under ***,the convergence rate of learning models in the decentralized scheme depends on the network topology *** propose optimizing the topology design to improve training efficiency for decentralized federated learning,which is a non-trivial problem,especially when considering data *** this paper,we first demonstrate the advantage of hypercube topology and present a hypercube graph construction method to reduce data heterogeneity by carefully selecting neighbors of each training device—a process that resembles classic graph *** addition,we propose a heuristic method for generating torus ***,we have explored the communication patterns in hypercube topology and propose a sequential synchronization scheme to reduce communication cost during training.A batch synchronization scheme is presented to fine-tune the communication pattern for hypercube *** on real-world datasets show that our proposed graph construction methods can accelerate the training process,and our sequential synchronization scheme can significantly reduce the overall communication traffic during training.

关键词： data heterogeneity decentralized federated learning graph embedding network topology

Dual-blockchain based multi-layer grouping federated learning scheme for heterogeneous data in industrial IoT

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Blockchain(Research and Applications) 2024年第3期5卷 11-21页

作者： Xin Wang Haoji Zhang Haoyu Wu Hongnian Yu School of Electronic Information and Artificial Intelligence Shaanxi University of Science&TechnologyXi’an710021China School of Engineering and the Built Environment Edinburgh Napier UniversityEdinburghEH105DTUnited Kingdom

Federated learning(FL)allows data owners to train neural networks together without sharing local data,allowing the industrial Internet of Things(IIoT)to share a variety of ***,traditional FL frameworks suffer from data heterogeneity and outdated *** address these issues,this paper proposes a dualblockchain based multi-layer grouping federated learning(BMFL)*** divides the participant groups based on the training tasks,then realizes the model training by combining synchronous and asynchronous FL through the multi-layer grouping structure,and uses the model blockchain to record the characteristic tags of the global model,allowing group-manners to extract the model based on the feature requirements and solving the problem of data *** addition,to protect the privacy of the model gradient parameters and manage the key,the global model is stored in ciphertext,and the chameleon hash algorithm is used to perform the modification and management of the encrypted key on the key blockchain while keeping the block header hash ***,we evaluate the performance of BMFL on different public datasets and verify the practicality of the scheme with real fault *** experimental results show that the proposed BMFL exhibits more stable and accurate convergence behavior than the classic FL algorithm,and the key revocation overhead time is reasonable.

关键词： Federal learning Multi-level grouping Editable blockchain data heterogeneity Industrial Internet of Things(IIOT)

Federated Learning for 6G:A Survey From Perspective of Integrated Sensing,Communication and Computation

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ZTE Communications 2023年第2期21卷 25-33页

作者： ZHAO Moke HUANG Yansong LI Xuan Beijing University of Posts and Telecommunications Beijing 100876China

With the rapid advancements in edge computing and artificial intelligence,federated learning(FL)has gained momentum as a promising approach to collaborative data utilization across organizations and devices,while ensuring data privacy and information *** order to further harness the energy efficiency of wireless networks,an integrated sensing,communication and computation(ISCC)framework has been proposed,which is anticipated to be a key enabler in the era of 6G *** the advantages of pushing intelligence to edge devices are multi-fold,some challenges arise when incorporating FL into wireless networks under the umbrella of *** paper provides a comprehensive survey of FL,with special emphasis on the design and optimization of *** commence by introducing the background and fundamentals of FL and the ISCC ***,the aforementioned challenges are highlighted and the state of the art in potential solutions is ***,design guidelines are provided for the incorporation of FL and ***,this paper aims to contribute to the understanding of FL in the context of wireless networks,with a focus on the ISCC framework,and provide insights into addressing the challenges and optimizing the design for the integration of FL into future 6G networks.

关键词： integrated sensing communication and computation federated learning data heterogeneity limited resources

A Survey of Federated Learning on Non-IID data

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ZTE Communications 2022年第3期20卷 17-26页

作者： HAN Xuming GAO Minghan WANG Limin HE Zaobo WANG Yanze Jinan University Guangzhou 510632China Changchun University of Technology Changchun 130012China Guangdong University of Finance&Economics Guangzhou 510320China

Federated learning(FL) is a machine learning paradigm for data silos and privacy protection,which aims to organize multiple clients for training global machine learning models without exposing data to all ***,when dealing with non-independently identically distributed(non-ⅡD) client data,FL cannot obtain more satisfactory results than centrally trained machine learning and even fails to match the accuracy of the local model obtained by client training *** analyze and address the above issues,we survey the state-of-theart methods in the literature related to FL on non-ⅡD *** this basis,a motivation-based taxonomy,which classifies these methods into two categories,including heterogeneity reducing strategies and adaptability enhancing strategies,is ***,the core ideas and main challenges of these methods are ***,we envision several promising research directions that have not been thoroughly studied,in hope of promoting research in related fields to a certain extent.

关键词： data heterogeneity federated learning non-IID data