Federated Learning(FL)has become a leading decentralized solution that enables multiple clients to train a model in a collaborative environment without directly sharing raw data,making it suitable for privacy-sensitiv...Federated Learning(FL)has become a leading decentralized solution that enables multiple clients to train a model in a collaborative environment without directly sharing raw data,making it suitable for privacy-sensitive applications such as healthcare,finance,and smart systems.As the field continues to evolve,the research field has become more complex and scattered,covering different system designs,training methods,and privacy techniques.This survey is organized around the three core challenges:how the data is distributed,how models are synchronized,and how to defend against attacks.It provides a structured and up-to-date review of FL research from 2023 to 2025,offering a unified taxonomy that categorizes works by data distribution(Horizontal FL,Vertical FL,Federated Transfer Learning,and Personalized FL),training synchronization(synchronous and asynchronous FL),optimization strategies,and threat models(data leakage and poisoning attacks).In particular,we summarize the latest contributions in Vertical FL frameworks for secure multi-party learning,communication-efficient Horizontal FL,and domain-adaptive Federated Transfer Learning.Furthermore,we examine synchronization techniques addressing system heterogeneity,including straggler mitigation in synchronous FL and staleness management in asynchronous FL.The survey covers security threats in FL,such as gradient inversion,membership inference,and poisoning attacks,as well as their defense strategies that include privacy-preserving aggregation and anomaly detection.The paper concludes by outlining unresolved issues and highlighting challenges in handling personalized models,scalability,and real-world adoption.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
Federated learning(FL)is a novel distributed machine learning paradigm that enables participants to collaboratively train a centralized model with privacy preservation by eliminating the requirement of data sharing.In...Federated learning(FL)is a novel distributed machine learning paradigm that enables participants to collaboratively train a centralized model with privacy preservation by eliminating the requirement of data sharing.In practice,FL often involves multiple participants and requires the third party to aggregate global information to guide the update of the target participant.Therefore,many FL methods do not work well due to the training and test data of each participant may not be sampled from the same feature space and the same underlying distribution.Meanwhile,the differences in their local devices(system heterogeneity),the continuous influx of online data(incremental data),and labeled data scarcity may further influence the performance of these methods.To solve this problem,federated transfer learning(FTL),which integrates transfer learning(TL)into FL,has attracted the attention of numerous researchers.However,since FL enables a continuous share of knowledge among participants with each communication round while not allowing local data to be accessed by other participants,FTL faces many unique challenges that are not present in TL.In this survey,we focus on categorizing and reviewing the current progress on federated transfer learning,and outlining corresponding solutions and applications.Furthermore,the common setting of FTL scenarios,available datasets,and significant related research are summarized in this survey.展开更多
文摘Federated Learning(FL)has become a leading decentralized solution that enables multiple clients to train a model in a collaborative environment without directly sharing raw data,making it suitable for privacy-sensitive applications such as healthcare,finance,and smart systems.As the field continues to evolve,the research field has become more complex and scattered,covering different system designs,training methods,and privacy techniques.This survey is organized around the three core challenges:how the data is distributed,how models are synchronized,and how to defend against attacks.It provides a structured and up-to-date review of FL research from 2023 to 2025,offering a unified taxonomy that categorizes works by data distribution(Horizontal FL,Vertical FL,Federated Transfer Learning,and Personalized FL),training synchronization(synchronous and asynchronous FL),optimization strategies,and threat models(data leakage and poisoning attacks).In particular,we summarize the latest contributions in Vertical FL frameworks for secure multi-party learning,communication-efficient Horizontal FL,and domain-adaptive Federated Transfer Learning.Furthermore,we examine synchronization techniques addressing system heterogeneity,including straggler mitigation in synchronous FL and staleness management in asynchronous FL.The survey covers security threats in FL,such as gradient inversion,membership inference,and poisoning attacks,as well as their defense strategies that include privacy-preserving aggregation and anomaly detection.The paper concludes by outlining unresolved issues and highlighting challenges in handling personalized models,scalability,and real-world adoption.
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.
基金the National Key R&D Program of China(No.2021ZD0113602)the National Natural Science Foundation of China(Grant Nos.62176014 and 62202273)。
文摘Federated learning(FL)is a novel distributed machine learning paradigm that enables participants to collaboratively train a centralized model with privacy preservation by eliminating the requirement of data sharing.In practice,FL often involves multiple participants and requires the third party to aggregate global information to guide the update of the target participant.Therefore,many FL methods do not work well due to the training and test data of each participant may not be sampled from the same feature space and the same underlying distribution.Meanwhile,the differences in their local devices(system heterogeneity),the continuous influx of online data(incremental data),and labeled data scarcity may further influence the performance of these methods.To solve this problem,federated transfer learning(FTL),which integrates transfer learning(TL)into FL,has attracted the attention of numerous researchers.However,since FL enables a continuous share of knowledge among participants with each communication round while not allowing local data to be accessed by other participants,FTL faces many unique challenges that are not present in TL.In this survey,we focus on categorizing and reviewing the current progress on federated transfer learning,and outlining corresponding solutions and applications.Furthermore,the common setting of FTL scenarios,available datasets,and significant related research are summarized in this survey.
