Cation-π interactions secure aggregation induced emission of planar organic luminophores.
Sign Up to like & getrecommendations! Published in 2019 at "Chemical communications"
DOI: 10.1039/c9cc06829e
Abstract: The use of non-covalent intermolecular π+-π interactions between quaternary pyridinium or imidazolium cations and aromatic π systems is an efficient approach to achieve AIE in planar purely organic luminophores. read more here.
Keywords: planar; secure aggregation; interactions secure; aggregation induced ... See more keywords
BalancedSecAgg: Toward Fast Secure Aggregation for Federated Learning
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Access"
DOI: 10.1109/access.2024.3491779
Abstract: Federated learning is a promising collaborative learning system from the perspective of training data privacy preservation; however, there is a risk of privacy leakage from individual local models of users. Secure aggregation protocols based on… read more here.
Keywords: secure aggregation; random masks; aggregation; federated learning ... See more keywords
VSAFL: Verifiable and Secure Aggregation With (Poly) Logarithmic Overhead in Federated Learning
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2024.3449705
Abstract: Federated learning (FL) is a distributed machine learning framework that enables multiple participants to train models without directly sharing local data. However, sensitive information about participants may still be leaked through their gradients. Furthermore, centralized… read more here.
Keywords: verifiable secure; secure aggregation; vsafl; vsafl verifiable ... See more keywords
Robust and Secure Aggregation Scheme for Federated Learning
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2024.3509222
Abstract: Federated learning (FL) with a distributed trust framework effectively mitigates centralized security risks. However, it remains vulnerable to in-protocol Denial-of-Service attacks, resulting in the malicious server refusing to aggregate the valid gradients or terminating the… read more here.
Keywords: aggregation scheme; federated learning; secure aggregation; robust secure ... See more keywords
Secure Aggregation in Heterogeneous Federated Learning for Digital Ecosystems
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Transactions on Consumer Electronics"
DOI: 10.1109/tce.2023.3330501
Abstract: Privacy-preserving federated learning (PPFL) is vital for Industry 5.0 digital ecosystems due to the increasing number of interconnected devices and the volume of shared sensitive data. Secure aggregation (SA) protocols are essential components to fulfill… read more here.
Keywords: aggregation heterogeneous; digital ecosystems; secure aggregation; federated learning ... See more keywords
Secure Aggregation for Clustered Federated Learning With Passive Adversaries
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Transactions on Communications"
DOI: 10.1109/tcomm.2024.3366394
Abstract: Clustered federated learning is a popular paradigm to tackle data heterogeneity in federated learning, by training personalized models for groups of users with similar data distributions. A critical challenge is to protect the privacy of… read more here.
Keywords: information; clustered federated; secure aggregation; user updates ... See more keywords
Efficient Full-Stack Private Federated Deep Learning With Post-Quantum Security
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Dependable and Secure Computing"
DOI: 10.1109/tdsc.2025.3568704
Abstract: Federated learning (FL) enables collaborative model training while preserving user data privacy by keeping data local. Despite these advantages, FL remains vulnerable to privacy attacks on user updates and model parameters during training and deployment.… read more here.
Keywords: post quantum; secure aggregation; privacy; math ... See more keywords
SAFELearning: Secure Aggregation in Federated Learning with Backdoor Detectability
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Information Forensics and Security"
DOI: 10.1109/tifs.2023.3280032
Abstract: For model privacy, local model parameters in federated learning shall be obfuscated before sent to the remote aggregator. This technique is referred to as secure aggregation. However, secure aggregation makes model poisoning attacks such as… read more here.
Keywords: aggregation; federated learning; secure aggregation; model ... See more keywords
RaSA: Robust and Adaptive Secure Aggregation for Edge-Assisted Hierarchical Federated Learning
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Information Forensics and Security"
DOI: 10.1109/tifs.2025.3559411
Abstract: Secure Aggregation (SA), in the Federated Learning (FL) setting, enables distributed clients to collaboratively learn a shared global model while keeping their raw data and local gradients private. However, when SA is implemented in edge-intelligence-driven… read more here.
Keywords: edge; rasa; secure aggregation; model ... See more keywords