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 …
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 …
Adaptive resource allocation for blockchain-based federated learning in Internet of Things
The fast development of mobile communication and artificial intelligence (AI) technologies
greatly promotes the prosperity of the Internet of Things (IoT), where various types of IoT …
greatly promotes the prosperity of the Internet of Things (IoT), where various types of IoT …
Symbiotic blockchain consensus: Cognitive backscatter communications-enabled wireless blockchain consensus
The wireless blockchain network (WBN) concept, born from the blockchain deployed in
wireless networks, has appealed to many network scenarios. Blockchain consensus …
wireless networks, has appealed to many network scenarios. Blockchain consensus …
Federated unlearning with momentum degradation
Data privacy is becoming increasingly important as data becomes more valuable, as
evidenced by the enactment of right-to-be-forgotten laws and regulations. However, in a …
evidenced by the enactment of right-to-be-forgotten laws and regulations. However, in a …
Addressing unreliable local models in federated learning through unlearning
Federated unlearning (FUL) is a promising solution for removing negative influences from
the global model. However, ensuring the reliability of local models in FL systems remains …
the global model. However, ensuring the reliability of local models in FL systems remains …
Balancing privacy protection and interpretability in federated learning
Federated learning (FL) aims to collaboratively train the global model in a distributed
manner by sharing the model parameters from local clients to a central server, thereby …
manner by sharing the model parameters from local clients to a central server, thereby …
Reinvigorating sustainability in Internet of Things marketing: Framework for multi-round real-time bidding with game machine learning
R Zhang, C Jiang, J Zhang, J Fan, J Ren, H Xia - Internet of Things, 2023 - Elsevier
Auction-based incentive mechanisms can satisfy the heterogeneous demands of both
Demand Side Platforms (DSP) and Supply Side Platforms (SSP) in Internet of Things (IoT) …
Demand Side Platforms (DSP) and Supply Side Platforms (SSP) in Internet of Things (IoT) …
Server-initiated federated unlearning to eliminate impacts of low-quality data
Federated unlearning (FUL) is an emerging distributed machine learning paradigm which
enables the removal or unlearning of specific training data effects from trained Federated …
enables the removal or unlearning of specific training data effects from trained Federated …
A visually secure image encryption method based on semi-tensor product compressed sensing and IWT-HD-SVD embedding
Z Shuo, H Pijun, C Yongguang, B Wang - Heliyon, 2023 - cell.com
The conventional approach for images encryption entails transforming a regular image into
an encrypted image that resembles noise. However, this noise-like encrypted image is …
an encrypted image that resembles noise. However, this noise-like encrypted image is …