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 …
Trustworthy federated learning: A survey
Federated Learning (FL) has 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 …
Multimodal federated learning: Concept, methods, applications and future directions
Multimodal learning mines and analyzes multimodal data in reality to better understand and
appreciate the world around people. However, how to exploit this rich multimodal data …
appreciate the world around people. However, how to exploit this rich multimodal data …
A survey on explainable artificial intelligence for cybersecurity
The “black-box” nature of artificial intelligence (AI) models has been the source of many
concerns in their use for critical applications. Explainable Artificial Intelligence (XAI) is a …
concerns in their use for critical applications. Explainable Artificial Intelligence (XAI) is a …
SVeriFL: Successive verifiable federated learning with privacy-preserving
H Gao, N He, T Gao - Information Sciences, 2023 - Elsevier
With federated learning, one of the most notable features is that it can update global model
parameter without using the users' local data. However, various security and privacy …
parameter without using the users' local data. However, various security and privacy …
A reinforcement learning model for the reliability of blockchain oracles
Smart contracts struggle with the major limitation of operating on data that is solely residing
on the blockchain network. The need of recruiting third parties, known as oracles, to assist …
on the blockchain network. The need of recruiting third parties, known as oracles, to assist …
Trust-augmented deep reinforcement learning for federated learning client selection
In the context of distributed machine learning, the concept of federated learning (FL) has
emerged as a solution to the privacy concerns that users have about sharing their own data …
emerged as a solution to the privacy concerns that users have about sharing their own data …
FedRL: a reinforcement learning federated recommender system for efficient communication using reinforcement selector and hypernet generator
The field of recommender systems aims to predict users' latent interests by analyzing their
preferences and behaviors. However, privacy concerns about user data collection lead to …
preferences and behaviors. However, privacy concerns about user data collection lead to …
Secure and privacy-preserving decentralized federated learning for personalized recommendations in consumer electronics using blockchain and homomorphic …
BB Gupta, A Gaurav, V Arya - IEEE Transactions on Consumer …, 2023 - ieeexplore.ieee.org
Over the past few years, personalized recommendations have emerged as a fundamental
component of the consumer electronics sector. The rise of decentralized federated learning …
component of the consumer electronics sector. The rise of decentralized federated learning …
Fedmint: Intelligent bilateral client selection in federated learning with newcomer iot devices
Federated learning (FL) is a novel distributed privacy-preserving learning paradigm, which
enables the collaboration among several participants (eg, Internet of Things (IoT) devices) …
enables the collaboration among several participants (eg, Internet of Things (IoT) devices) …