Federated learning(FL)is a distributed machine learning(ML)framework where several clients cooperatively train an ML model by exchanging the model parameters without directly sharing their local data.In FL,the limited...Federated learning(FL)is a distributed machine learning(ML)framework where several clients cooperatively train an ML model by exchanging the model parameters without directly sharing their local data.In FL,the limited number of participants for model aggregation and communication latency are two major bottlenecks.Hierarchical federated learning(HFL),with a cloud-edge-client hierarchy,can leverage the large coverage of cloud servers and the low transmission latency of edge servers.There are growing research interests in implementing FL in vehicular networks due to the requirements of timely ML training for intelligent vehicles.However,the limited number of participants in vehicular networks and vehicle mobility degrade the performance of FL training.In this context,HFL,which stands out for lower latency,wider coverage and more participants,is promising in vehicular networks.In this paper,we begin with the background and motivation of HFL and the feasibility of implementing HFL in vehicular networks.Then,the architecture of HFL is illustrated.Next,we clarify new issues in HFL and review several existing solutions.Furthermore,we introduce some typical use cases in vehicular networks as well as our initial efforts on implementing HFL in vehicular networks.Finally,we conclude with future research directions.展开更多
In the context of edge computing environments in general and the metaverse in particular,federated learning(FL)has emerged as a distributed machine learning paradigm that allows multiple users to collaborate on traini...In the context of edge computing environments in general and the metaverse in particular,federated learning(FL)has emerged as a distributed machine learning paradigm that allows multiple users to collaborate on training a shared machine learning model locally,eliminating the need for uploading raw data to a central server.It is perhaps the only training paradigm that preserves the privacy of user data,which is essential for computing environments as personal as the metaverse.However,the original FL architecture proposed is not scalable to a large number of user devices in the metaverse community.To mitigate this problem,hierarchical federated learning(HFL)has been introduced as a general distributed learning paradigm,inspiring a number of research works.In this paper,we present several types of HFL architectures,with a special focus on the three-layer client-edge-cloud HFL architecture,which is most pertinent to the metaverse due to its delay-sensitive nature.We also examine works that take advantage of the natural layered organization of three-layer client-edge-cloud HFL to tackle some of the most challenging problems in FL within the metaverse.Finally,we outline some future research directions of HFL in the metaverse.展开更多
Hierarchical Federated Learning(HFL)extends traditional Federated Learning(FL)by introducing multi-level aggregation in which model updates pass through clients,edge servers,and a global server.While this hierarchical...Hierarchical Federated Learning(HFL)extends traditional Federated Learning(FL)by introducing multi-level aggregation in which model updates pass through clients,edge servers,and a global server.While this hierarchical structure enhances scalability,it also increases vulnerability to adversarial attacks—such as data poisoning and model poisoning—that disrupt learning by introducing discrepancies at the edge server level.These discrepancies propagate through aggregation,affecting model consistency and overall integrity.Existing studies on adversarial behaviour in FL primarily rely on single-metric approaches—such as cosine similarity or Euclidean distance—to assess model discrepancies and filter out anomalous updates.However,these methods fail to capture the diverse ways adversarial attacks influence model updates,particularly in highly heterogeneous data environments and hierarchical structures.Attackers can exploit the limitations of single-metric defences by crafting updates that seem benign under one metric while remaining anomalous under another.Moreover,prior studies have not systematically analysed how model discrepancies evolve over time,vary across regions,or affect clustering structures in HFL architectures.To address these limitations,we propose the Model Discrepancy Score(MDS),a multi-metric framework that integrates Dissimilarity,Distance,Uncorrelation,and Divergence to provide a comprehensive analysis of how adversarial activity affects model discrepancies.Through temporal,spatial,and clustering analyses,we examine how attacks affect model discrepancies at the edge server level in 3LHFL and 4LHFL architectures and evaluate MDS’s ability to distinguish between benign and malicious servers.Our results show that while 4LHFL effectively mitigates discrepancies in regional attack scenarios,it struggles with distributed attacks due to additional aggregation layers that obscure distinguishable discrepancy patterns over time,across regions,and within clustering structures.Factors influencing detection include data heterogeneity,attack sophistication,and hierarchical aggregation depth.These findings highlight the limitations of single-metric approaches and emphasize the need for multi-metric strategies such as MDS to enhance HFL security.展开更多
In recent research on the Digital Twin-based Vehicular Ad hoc Network(DT-VANET),Federated Learning(FL)has shown its ability to provide data privacy.However,Federated learning struggles to adequately train a global mod...In recent research on the Digital Twin-based Vehicular Ad hoc Network(DT-VANET),Federated Learning(FL)has shown its ability to provide data privacy.However,Federated learning struggles to adequately train a global model when confronted with data heterogeneity and data sparsity among vehicles,which ensure suboptimal accuracy in making precise predictions for different vehicle types.To address these challenges,this paper combines Federated Transfer Learning(FTL)to conduct vehicle clustering related to types of vehicles and proposes a novel Hierarchical Federated Transfer Learning(HFTL).We construct a framework for DT-VANET,along with two algorithms designed for cloud server model updates and intra-cluster federated transfer learning,to improve the accuracy of the global model.In addition,we developed a data quality score-based mechanism to prevent the global model from being affected by malicious vehicles.Lastly,detailed experiments on real-world datasets are conducted,considering different performance metrics that verify the effectiveness and efficiency of our algorithm.展开更多
基金sponsored in part by the National Key R&D Program of China under Grant No. 2020YFB1806605the National Natural Science Foundation of China under Grant Nos. 62022049, 62111530197, and 61871254+1 种基金OPPOsupported by the Fundamental Research Funds for the Central Universities under Grant No. 2022JBXT001
文摘Federated learning(FL)is a distributed machine learning(ML)framework where several clients cooperatively train an ML model by exchanging the model parameters without directly sharing their local data.In FL,the limited number of participants for model aggregation and communication latency are two major bottlenecks.Hierarchical federated learning(HFL),with a cloud-edge-client hierarchy,can leverage the large coverage of cloud servers and the low transmission latency of edge servers.There are growing research interests in implementing FL in vehicular networks due to the requirements of timely ML training for intelligent vehicles.However,the limited number of participants in vehicular networks and vehicle mobility degrade the performance of FL training.In this context,HFL,which stands out for lower latency,wider coverage and more participants,is promising in vehicular networks.In this paper,we begin with the background and motivation of HFL and the feasibility of implementing HFL in vehicular networks.Then,the architecture of HFL is illustrated.Next,we clarify new issues in HFL and review several existing solutions.Furthermore,we introduce some typical use cases in vehicular networks as well as our initial efforts on implementing HFL in vehicular networks.Finally,we conclude with future research directions.
文摘In the context of edge computing environments in general and the metaverse in particular,federated learning(FL)has emerged as a distributed machine learning paradigm that allows multiple users to collaborate on training a shared machine learning model locally,eliminating the need for uploading raw data to a central server.It is perhaps the only training paradigm that preserves the privacy of user data,which is essential for computing environments as personal as the metaverse.However,the original FL architecture proposed is not scalable to a large number of user devices in the metaverse community.To mitigate this problem,hierarchical federated learning(HFL)has been introduced as a general distributed learning paradigm,inspiring a number of research works.In this paper,we present several types of HFL architectures,with a special focus on the three-layer client-edge-cloud HFL architecture,which is most pertinent to the metaverse due to its delay-sensitive nature.We also examine works that take advantage of the natural layered organization of three-layer client-edge-cloud HFL to tackle some of the most challenging problems in FL within the metaverse.Finally,we outline some future research directions of HFL in the metaverse.
基金supported by the Technical and Vocational Training Corporation(TVTC)through the Saudi Arabian Culture Bureau(SACB)in the United Kingdom and the EPSRC-funded project National Edge AI Hub for Real Data:Edge Intelligence for Cyber-disturbances and Data Quality(EP/Y028813/1).
文摘Hierarchical Federated Learning(HFL)extends traditional Federated Learning(FL)by introducing multi-level aggregation in which model updates pass through clients,edge servers,and a global server.While this hierarchical structure enhances scalability,it also increases vulnerability to adversarial attacks—such as data poisoning and model poisoning—that disrupt learning by introducing discrepancies at the edge server level.These discrepancies propagate through aggregation,affecting model consistency and overall integrity.Existing studies on adversarial behaviour in FL primarily rely on single-metric approaches—such as cosine similarity or Euclidean distance—to assess model discrepancies and filter out anomalous updates.However,these methods fail to capture the diverse ways adversarial attacks influence model updates,particularly in highly heterogeneous data environments and hierarchical structures.Attackers can exploit the limitations of single-metric defences by crafting updates that seem benign under one metric while remaining anomalous under another.Moreover,prior studies have not systematically analysed how model discrepancies evolve over time,vary across regions,or affect clustering structures in HFL architectures.To address these limitations,we propose the Model Discrepancy Score(MDS),a multi-metric framework that integrates Dissimilarity,Distance,Uncorrelation,and Divergence to provide a comprehensive analysis of how adversarial activity affects model discrepancies.Through temporal,spatial,and clustering analyses,we examine how attacks affect model discrepancies at the edge server level in 3LHFL and 4LHFL architectures and evaluate MDS’s ability to distinguish between benign and malicious servers.Our results show that while 4LHFL effectively mitigates discrepancies in regional attack scenarios,it struggles with distributed attacks due to additional aggregation layers that obscure distinguishable discrepancy patterns over time,across regions,and within clustering structures.Factors influencing detection include data heterogeneity,attack sophistication,and hierarchical aggregation depth.These findings highlight the limitations of single-metric approaches and emphasize the need for multi-metric strategies such as MDS to enhance HFL security.
基金supported by the National Science Foundation(2343619,2416872,2244219,and 2146497).
文摘In recent research on the Digital Twin-based Vehicular Ad hoc Network(DT-VANET),Federated Learning(FL)has shown its ability to provide data privacy.However,Federated learning struggles to adequately train a global model when confronted with data heterogeneity and data sparsity among vehicles,which ensure suboptimal accuracy in making precise predictions for different vehicle types.To address these challenges,this paper combines Federated Transfer Learning(FTL)to conduct vehicle clustering related to types of vehicles and proposes a novel Hierarchical Federated Transfer Learning(HFTL).We construct a framework for DT-VANET,along with two algorithms designed for cloud server model updates and intra-cluster federated transfer learning,to improve the accuracy of the global model.In addition,we developed a data quality score-based mechanism to prevent the global model from being affected by malicious vehicles.Lastly,detailed experiments on real-world datasets are conducted,considering different performance metrics that verify the effectiveness and efficiency of our algorithm.
