946 research outputs found
Heterogeneous Federated Learning: State-of-the-art and Research Challenges
Federated learning (FL) has drawn increasing attention owing to its potential
use in large-scale industrial applications. Existing federated learning works
mainly focus on model homogeneous settings. However, practical federated
learning typically faces the heterogeneity of data distributions, model
architectures, network environments, and hardware devices among participant
clients. Heterogeneous Federated Learning (HFL) is much more challenging, and
corresponding solutions are diverse and complex. Therefore, a systematic survey
on this topic about the research challenges and state-of-the-art is essential.
In this survey, we firstly summarize the various research challenges in HFL
from five aspects: statistical heterogeneity, model heterogeneity,
communication heterogeneity, device heterogeneity, and additional challenges.
In addition, recent advances in HFL are reviewed and a new taxonomy of existing
HFL methods is proposed with an in-depth analysis of their pros and cons. We
classify existing methods from three different levels according to the HFL
procedure: data-level, model-level, and server-level. Finally, several critical
and promising future research directions in HFL are discussed, which may
facilitate further developments in this field. A periodically updated
collection on HFL is available at https://github.com/marswhu/HFL_Survey.Comment: 42 pages, 11 figures, and 4 table
Federated Learning for Iot/Edge/Fog Computing Systems
With the help of a new architecture called Edge/Fog (E/F) computing, cloud
computing services can now be extended nearer to data generator devices. E/F
computing in combination with Deep Learning (DL) is a promisedtechnique that is
vastly applied in numerous fields. To train their models, data producers in
conventional DL architectures with E/F computing enable them to repeatedly
transmit and communicate data with third-party servers, like Edge/Fog or cloud
servers. Due to the extensive bandwidth needs, legal issues, and privacy risks,
this architecture is frequently impractical. Through a centralized server, the
models can be co-trained by FL through distributed clients, including cars,
hospitals, and mobile phones, while preserving data localization. As it
facilitates group learning and model optimization, FL can therefore be seen as
a motivating element in the E/F computing paradigm. Although FL applications in
E/F computing environments have been considered in previous studies, FL
execution and hurdles in the E/F computing framework have not been thoroughly
covered. In order to identify advanced solutions, this chapter will provide a
review of the application of FL in E/F computing systems. We think that by
doing this chapter, researchers will learn more about how E/F computing and FL
enable related concepts and technologies. Some case studies about the
implementation of federated learning in E/F computing are being investigated.
The open issues and future research directions are introduced.Comment: 21 pages, 4 figures, Book chapte
Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems
Intelligent transportation systems (ITSs) have been fueled by the rapid
development of communication technologies, sensor technologies, and the
Internet of Things (IoT). Nonetheless, due to the dynamic characteristics of
the vehicle networks, it is rather challenging to make timely and accurate
decisions of vehicle behaviors. Moreover, in the presence of mobile wireless
communications, the privacy and security of vehicle information are at constant
risk. In this context, a new paradigm is urgently needed for various
applications in dynamic vehicle environments. As a distributed machine learning
technology, federated learning (FL) has received extensive attention due to its
outstanding privacy protection properties and easy scalability. We conduct a
comprehensive survey of the latest developments in FL for ITS. Specifically, we
initially research the prevalent challenges in ITS and elucidate the
motivations for applying FL from various perspectives. Subsequently, we review
existing deployments of FL in ITS across various scenarios, and discuss
specific potential issues in object recognition, traffic management, and
service providing scenarios. Furthermore, we conduct a further analysis of the
new challenges introduced by FL deployment and the inherent limitations that FL
alone cannot fully address, including uneven data distribution, limited storage
and computing power, and potential privacy and security concerns. We then
examine the existing collaborative technologies that can help mitigate these
challenges. Lastly, we discuss the open challenges that remain to be addressed
in applying FL in ITS and propose several future research directions
Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights
Artificial Intelligence (AI) is expected to play an instrumental role in the
next generation of wireless systems, such as sixth-generation (6G) mobile
network. However, massive data, energy consumption, training complexity, and
sensitive data protection in wireless systems are all crucial challenges that
must be addressed for training AI models and gathering intelligence and
knowledge from distributed devices. Federated Learning (FL) is a recent
framework that has emerged as a promising approach for multiple learning agents
to build an accurate and robust machine learning models without sharing raw
data. By allowing mobile handsets and devices to collaboratively learn a global
model without explicit sharing of training data, FL exhibits high privacy and
efficient spectrum utilization. While there are a lot of survey papers
exploring FL paradigms and usability in 6G privacy, none of them has clearly
addressed how FL can be used to improve the protocol stack and wireless
operations. The main goal of this survey is to provide a comprehensive overview
on FL usability to enhance mobile services and enable smart ecosystems to
support novel use-cases. This paper examines the added-value of implementing FL
throughout all levels of the protocol stack. Furthermore, it presents important
FL applications, addresses hot topics, provides valuable insights and explicits
guidance for future research and developments. Our concluding remarks aim to
leverage the synergy between FL and future 6G, while highlighting FL's
potential to revolutionize wireless industry and sustain the development of
cutting-edge mobile services.Comment: 32 pages, 7 figures; 9 Table
Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges
Machine learning (ML) is widely used for key tasks in Connected and Automated
Vehicles (CAV), including perception, planning, and control. However, its
reliance on vehicular data for model training presents significant challenges
related to in-vehicle user privacy and communication overhead generated by
massive data volumes. Federated learning (FL) is a decentralized ML approach
that enables multiple vehicles to collaboratively develop models, broadening
learning from various driving environments, enhancing overall performance, and
simultaneously securing local vehicle data privacy and security. This survey
paper presents a review of the advancements made in the application of FL for
CAV (FL4CAV). First, centralized and decentralized frameworks of FL are
analyzed, highlighting their key characteristics and methodologies. Second,
diverse data sources, models, and data security techniques relevant to FL in
CAVs are reviewed, emphasizing their significance in ensuring privacy and
confidentiality. Third, specific and important applications of FL are explored,
providing insight into the base models and datasets employed for each
application. Finally, existing challenges for FL4CAV are listed and potential
directions for future work are discussed to further enhance the effectiveness
and efficiency of FL in the context of CAV
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