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 …
Survey on federated learning for intrusion detection system: Concept, architectures, aggregation strategies, challenges, and future directions
Intrusion Detection Systems (IDS) are essential for securing computer networks by
identifying and mitigating potential threats. However, traditional IDS face challenges related …
identifying and mitigating potential threats. However, traditional IDS face challenges related …
[HTML][HTML] A review of federated learning in agriculture
KR Žalik, M Žalik - Sensors, 2023 - mdpi.com
Federated learning (FL), with the aim of training machine learning models using data and
computational resources on edge devices without sharing raw local data, is essential for …
computational resources on edge devices without sharing raw local data, is essential for …
[HTML][HTML] Federated learning meets remote sensing
Remote sensing (RS) imagery provides invaluable insights into characterizing the Earth's
land surface within the scope of Earth observation (EO). Technological advances in capture …
land surface within the scope of Earth observation (EO). Technological advances in capture …
[HTML][HTML] A survey of security strategies in federated learning: Defending models, data, and privacy
Federated Learning (FL) has emerged as a transformative paradigm in machine learning,
enabling decentralized model training across multiple devices while preserving data …
enabling decentralized model training across multiple devices while preserving data …
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 …
[HTML][HTML] Centralised vs. decentralised federated load forecasting in smart buildings: Who holds the key to adversarial attack robustness?
The integration of AI and ML into energy forecasting is crucial for modern energy
management. Federated Learning (FL) is particularly noteworthy because it enhances data …
management. Federated Learning (FL) is particularly noteworthy because it enhances data …
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 …
Trustworthy federated learning: A comprehensive review, architecture, key challenges, and future research prospects
Federated Learning (FL) emerged as a significant advancement in the field of Artificial
Intelligence (AI), enabling collaborative model training across distributed devices while …
Intelligence (AI), enabling collaborative model training across distributed devices while …