Model optimization techniques in personalized federated learning: A survey
Personalized federated learning (PFL) is an exciting approach that allows machine learning
(ML) models to be trained on diverse and decentralized sources of data, while maintaining …
(ML) models to be trained on diverse and decentralized sources of data, while maintaining …
Personalized federation learning with model-contrastive learning for multi-modal user modeling in human-centric metaverse
With the flourish of digital technologies and rapid development of 5G and beyond networks,
Metaverse has become an increasingly hotly discussed topic, which offers users with …
Metaverse has become an increasingly hotly discussed topic, which offers users with …
A comprehensive review on Federated Learning for Data-Sensitive Application: Open issues & challenges
Abstract Artificial intelligence employs Machine Learning (ML) and Deep Learning (DL) to
analyze data. In both, the data is stored centrally. The data involved may be sensitive and …
analyze data. In both, the data is stored centrally. The data involved may be sensitive and …
Joint age-based client selection and resource allocation for communication-efficient federated learning over noma networks
In federated learning (FL), distributed clients can collaboratively train a shared global model
while retaining their own training data locally. Nevertheless, the performance of FL is often …
while retaining their own training data locally. Nevertheless, the performance of FL is often …
A multifaceted survey on federated learning: Fundamentals, paradigm shifts, practical issues, recent developments, partnerships, trade-offs, trustworthiness, and ways …
A Majeed, SO Hwang - IEEE Access, 2024 - ieeexplore.ieee.org
Federated learning (FL) is considered a de facto standard for privacy preservation in AI
environments because it does not require data to be aggregated in some central place to …
environments because it does not require data to be aggregated in some central place to …
Resource-aware multi-criteria vehicle participation for federated learning in Internet of vehicles
J Wen, J Zhang, Z Zhang, Z Cui, X Cai, J Chen - Information Sciences, 2024 - Elsevier
Federated learning (FL), as a safe distributed training mode, provides strong support for the
edge intelligence of the Internet of Vehicles (IoV) to realize efficient collaborative control and …
edge intelligence of the Internet of Vehicles (IoV) to realize efficient collaborative control and …
Convergence Analysis and Latency Minimization for Semi-Federated Learning in Massive IoT Networks
J Ren, W Ni, H Tian, G Nie - IEEE Transactions on Green …, 2023 - ieeexplore.ieee.org
As the number of sensors becomes massive in Internet of Things (IoT) networks, the amount
of data is humongous. To process data in real-time while protecting user privacy, federated …
of data is humongous. To process data in real-time while protecting user privacy, federated …
Multi-server federated learning for buildings energy prediction with wind speed
Energy consumption of buildings is correlated with environmental factors such as weather
and temperature. Developing a reliable energy forecasting model requires a large-scale …
and temperature. Developing a reliable energy forecasting model requires a large-scale …
REWAFL: Residual Energy and Wireless Aware Participant Selection for Efficient Federated Learning Over Mobile Devices
Participant selection (PS) helps to accelerate federated learning (FL) convergence, which is
essential for the practical deployment of FL over mobile devices. While most existing PS …
essential for the practical deployment of FL over mobile devices. While most existing PS …
Pre-Training and Personalized Fine-Tuning via Over-the-Air Federated Meta-Learning: Convergence-Generalization Trade-Offs
For modern artificial intelligence (AI) applications such as large language models (LLMs),
the training paradigm has recently shifted to pre-training followed by fine-tuning …
the training paradigm has recently shifted to pre-training followed by fine-tuning …