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 …
(CAV), including perception, planning, and control. However, its reliance on vehicular data …
Intrusion Detection based on Federated Learning: a systematic review
JL Hernandez-Ramos, G Karopoulos… - arXiv preprint arXiv …, 2023 - arxiv.org
The evolution of cybersecurity is undoubtedly associated and intertwined with the
development and improvement of artificial intelligence (AI). As a key tool for realizing more …
development and improvement of artificial intelligence (AI). As a key tool for realizing more …
Personalized and privacy-preserving federated heterogeneous medical image analysis with PPPML-HMI
Heterogeneous data is endemic due to the use of diverse models and settings of devices by
hospitals in the field of medical imaging. However, there are few open-source frameworks …
hospitals in the field of medical imaging. However, there are few open-source frameworks …
DeTA: Minimizing Data Leaks in Federated Learning via Decentralized and Trustworthy Aggregation
Federated learning (FL) relies on a central authority to oversee and aggregate model
updates contributed by multiple participating parties in the training process. This …
updates contributed by multiple participating parties in the training process. This …
Fedmfs: Federated multimodal fusion learning with selective modality communication
Federated learning (FL) is a distributed machine learning (ML) paradigm that enables clients
to collaborate without accessing, infringing upon, or leaking original user data by sharing …
to collaborate without accessing, infringing upon, or leaking original user data by sharing …
Dfedadmm: Dual constraints controlled model inconsistency for decentralized federated learning
To address the communication burden issues associated with federated learning (FL),
decentralized federated learning (DFL) discards the central server and establishes a …
decentralized federated learning (DFL) discards the central server and establishes a …
Submodel partitioning in hierarchical federated learning: Algorithm design and convergence analysis
Hierarchical federated learning (HFL) has demonstrated promising scalability advantages
over the traditional" star-topology" architecture-based federated learning (FL). However, HFL …
over the traditional" star-topology" architecture-based federated learning (FL). However, HFL …
Asymmetrically decentralized federated learning
To address the communication burden and privacy concerns associated with the centralized
server in Federated Learning (FL), Decentralized Federated Learning (DFL) has emerged …
server in Federated Learning (FL), Decentralized Federated Learning (DFL) has emerged …
Communication-efficient multimodal federated learning: Joint modality and client selection
Multimodal federated learning (FL) aims to enrich model training in FL settings where clients
are collecting measurements across multiple modalities. However, key challenges to …
are collecting measurements across multiple modalities. However, key challenges to …
Digital ethics in federated learning
The Internet of Things (IoT) consistently generates vast amounts of data, sparking increasing
concern over the protection of data privacy and the limitation of data misuse. Federated …
concern over the protection of data privacy and the limitation of data misuse. Federated …