[HTML][HTML] Limitations and future aspects of communication costs in federated learning: A survey
M Asad, S Shaukat, D Hu, Z Wang, E Javanmardi… - Sensors, 2023 - mdpi.com
This paper explores the potential for communication-efficient federated learning (FL) in
modern distributed systems. FL is an emerging distributed machine learning technique that …
modern distributed systems. FL is an emerging distributed machine learning technique that …
A review on client selection models in federated learning
Federated learning (FL) is a decentralized machine learning (ML) technique that enables
multiple clients to collaboratively train a common ML model without them having to share …
multiple clients to collaboratively train a common ML model without them having to share …
Federated learning for healthcare applications
Due to the fast advancement of artificial intelligence (AI), centralized-based models have
become critical for healthcare tasks like in medical image analysis and human behavior …
become critical for healthcare tasks like in medical image analysis and human behavior …
Exploring the applications and security threats of Internet of Thing in the cloud computing paradigm: A comprehensive study on the cloud of things
Abstract The term “Internet of Things”(IoT) represents a vast interconnected network
comprising ordinary objects enhanced with electronics like sensors, actuators, and wireless …
comprising ordinary objects enhanced with electronics like sensors, actuators, and wireless …
[HTML][HTML] Federated learning enables 6 G communication technology: Requirements, applications, and integrated with intelligence framework
The 5 G networks are effectively deployed worldwide, and academia and industries have
begun looking at 6 G network communication technology for consumer electronics …
begun looking at 6 G network communication technology for consumer electronics …
Advances and open challenges in federated learning with foundation models
The integration of Foundation Models (FMs) with Federated Learning (FL) presents a
transformative paradigm in Artificial Intelligence (AI), offering enhanced capabilities while …
transformative paradigm in Artificial Intelligence (AI), offering enhanced capabilities while …
Traceable Federated Continual Learning
Q Wang, B Liu, Y Li - … of the IEEE/CVF Conference on …, 2024 - openaccess.thecvf.com
Federated continual learning (FCL) is a typical mechanism to achieve collaborative model
training among clients that own dynamic data. While traditional FCL methods have been …
training among clients that own dynamic data. While traditional FCL methods have been …
Image-based molecular representation learning for drug development: a survey
Y Li, B Liu, J Deng, Y Guo, H Du - Briefings in Bioinformatics, 2024 - academic.oup.com
Artificial intelligence (AI) powered drug development has received remarkable attention in
recent years. It addresses the limitations of traditional experimental methods that are costly …
recent years. It addresses the limitations of traditional experimental methods that are costly …
On TinyML and Cybersecurity: Electric Vehicle Charging Infrastructure Use Case
As technology advances, the use of Machine Learning (ML) in cybersecurity is becoming
increasingly crucial to tackle the growing complexity of cyber threats. While traditional ML …
increasingly crucial to tackle the growing complexity of cyber threats. While traditional ML …
Beyond Fine-Tuning: Efficient and Effective Fed-Tuning for Mobile/Web Users
Fine-tuning is a typical mechanism to achieve model adaptation for mobile/web users,
where a model trained by the cloud is further retrained to fit the target user task. While …
where a model trained by the cloud is further retrained to fit the target user task. While …