Research graph
References from HFedM2ND: A secure Hierarchical Federated Learning framework against poisoning attacks with lightweight multi-layer malicious-node detection. Local targets link to admitted publications; unresolved targets remain external evidence.
Client-edge-cloud hierarchical federated learning
2020 · External reference
Communication-Efficient Learning of Deep Networks from Decentralized Data
2017 · External reference
Survey: federated learning data security and privacy-preserving in edge-internet of things
10.1007/s10462-024-10774-7 · 2024 · External reference
Towards federated learning at scale: System design
2019 · External reference
On the design of federated learning in the mobile edge computing systems
10.1109/tcomm.2021.3087125 · 2021 · External reference
HFEL: Joint edge association and resource allocation for cost-efficient hierarchical federated edge learning
10.1109/twc.2020.3003744 · 2020 · External reference
Mobility-aware reputation-based hierarchical federated learning for internet of vehicles
10.1109/tvt.2025.3609341 · 2026 · External reference
Hypernetworks-based hierarchical federated learning on hybrid non-IID datasets for digital twin in industrial IoT
10.1109/tnse.2023.3322701 · 2024 · External reference
Blockchain-based trustworthy and efficient hierarchical federated learning for UAV-enabled IoT networks
10.1109/jiot.2024.3370964 · 2024 · External reference
Analyzing federated learning through an adversarial lens
2019 · External reference
Unresolved reference
2019 · External reference
Attack of the tails: Yes, you really can backdoor federated learning
2020 · External reference
Local model poisoning attacks to Byzantine-Robust federated learning
2020 · External reference
Unresolved reference
2013 · External reference
10.1109/cvprw56347.2022.00383
10.1109/cvprw56347.2022.00383 · External reference
Label-flipping attacks in GNN-based federated learning
10.1109/tnse.2025.3528831 · 2025 · External reference
DisBezant: Secure and robust federated learning against Byzantine attack in IoT-enabled MTS
2023 · External reference
Analysis of deep learning under adversarial attacks in hierarchical federated learning
10.1016/j.hcc.2025.100321 · 2025 · External reference
Machine learning with adversaries: Byzantine tolerant gradient descent
2017 · External reference
Byzantine-robust distributed learning: Towards optimal statistical rates
2018 · External reference
SEAR: Secure and efficient aggregation for Byzantine-robust federated learning
10.1109/tdsc.2021.3093711 · 2022 · External reference
Robust aggregation for federated learning
10.1109/tsp.2022.3153135 · 2022 · External reference
Fltrust: Byzantine-robust federated learning via trust bootstrapping
2021 · External reference
Unresolved reference
2022 · External reference
FLDetector: Defending federated learning against model poisoning attacks via detecting malicious clients
2022 · External reference
MITDBA: Mitigating dynamic backdoor attacks in federated learning for IoT applications
10.1109/jiot.2023.3325634 · 2024 · External reference
FedDMC: Efficient and robust federated learning via detecting malicious clients
10.1109/tdsc.2024.3372634 · 2024 · External reference
Privacy-preserving federated learning resistant to byzantine attacks
2024 · External reference
Unresolved reference
2024 · External reference
SHIELD - secure aggregation against poisoning in hierarchical federated learning
10.1109/tdsc.2024.3472869 · 2025 · External reference
Toward robust hierarchical federated learning in internet of vehicles
10.1109/tits.2023.3243003 · 2023 · External reference
Towards robust and privacy-preserving federated learning in edge computing
10.1016/j.comnet.2024.110321 · 2024 · External reference
HFLMLD: Enhancing robustness in hierarchical federated learning with multiple layer defenses
10.1109/jiot.2025.3617774 · 2025 · External reference
Survey: federated learning data security and privacy-preserving in edge-internet of things
10.1007/s10462-024-10774-7 · ExternalCitation · doi-reference
Towards robust and privacy-preserving federated learning in edge computing
10.1016/j.comnet.2024.110321 · ExternalCitation · doi-reference
Analysis of deep learning under adversarial attacks in hierarchical federated learning
10.1016/j.hcc.2025.100321 · ExternalCitation · doi-reference
10.1109/cvprw56347.2022.00383
10.1109/cvprw56347.2022.00383 · ExternalCitation · doi-reference
MITDBA: Mitigating dynamic backdoor attacks in federated learning for IoT applications
10.1109/jiot.2023.3325634 · ExternalCitation · doi-reference
Blockchain-based trustworthy and efficient hierarchical federated learning for UAV-enabled IoT networks
10.1109/jiot.2024.3370964 · ExternalCitation · doi-reference
HFLMLD: Enhancing robustness in hierarchical federated learning with multiple layer defenses
10.1109/jiot.2025.3617774 · ExternalCitation · doi-reference
On the design of federated learning in the mobile edge computing systems
10.1109/tcomm.2021.3087125 · ExternalCitation · doi-reference
SEAR: Secure and efficient aggregation for Byzantine-robust federated learning
10.1109/tdsc.2021.3093711 · ExternalCitation · doi-reference
FedDMC: Efficient and robust federated learning via detecting malicious clients
10.1109/tdsc.2024.3372634 · ExternalCitation · doi-reference
SHIELD - secure aggregation against poisoning in hierarchical federated learning
10.1109/tdsc.2024.3472869 · ExternalCitation · doi-reference
Toward robust hierarchical federated learning in internet of vehicles
10.1109/tits.2023.3243003 · ExternalCitation · doi-reference
Hypernetworks-based hierarchical federated learning on hybrid non-IID datasets for digital twin in industrial IoT
10.1109/tnse.2023.3322701 · ExternalCitation · doi-reference
Label-flipping attacks in GNN-based federated learning
10.1109/tnse.2025.3528831 · ExternalCitation · doi-reference
Robust aggregation for federated learning
10.1109/tsp.2022.3153135 · ExternalCitation · doi-reference
Mobility-aware reputation-based hierarchical federated learning for internet of vehicles
10.1109/tvt.2025.3609341 · ExternalCitation · doi-reference
HFEL: Joint edge association and resource allocation for cost-efficient hierarchical federated edge learning
10.1109/twc.2020.3003744 · ExternalCitation · doi-reference
