Guardado en:
| Autores principales: | Khan, Afsana, Thij, Marijn ten, Tang, Guangzhi, Wilbik, Anna |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2602.19207 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
VFL-RPS: Relevant Participant Selection in Vertical Federated Learning
por: Khan, Afsana, et al.
Publicado: (2025)
por: Khan, Afsana, et al.
Publicado: (2025)
Incentive Allocation in Vertical Federated Learning Based on Bankruptcy Problem
por: Khan, Afsana, et al.
Publicado: (2023)
por: Khan, Afsana, et al.
Publicado: (2023)
ZK-HybridFL: Zero-Knowledge Proof-Enhanced Hybrid Ledger for Federated Learning
por: Taherpour, Amirhossein, et al.
Publicado: (2026)
por: Taherpour, Amirhossein, et al.
Publicado: (2026)
IP-FL: Incentivized and Personalized Federated Learning
por: Khan, Ahmad Faraz, et al.
Publicado: (2023)
por: Khan, Ahmad Faraz, et al.
Publicado: (2023)
Empirical Capacity Model for Self-Attention Neural Networks
por: Härmä, Aki, et al.
Publicado: (2024)
por: Härmä, Aki, et al.
Publicado: (2024)
CyclicFL: A Cyclic Model Pre-Training Approach to Efficient Federated Learning
por: Zhang, Pengyu, et al.
Publicado: (2023)
por: Zhang, Pengyu, et al.
Publicado: (2023)
GeFL: Model-Agnostic Federated Learning with Generative Models
por: Kang, Honggu, et al.
Publicado: (2024)
por: Kang, Honggu, et al.
Publicado: (2024)
MAR-FL: A Communication Efficient Peer-to-Peer Federated Learning System
por: Mulitze, Felix, et al.
Publicado: (2025)
por: Mulitze, Felix, et al.
Publicado: (2025)
Multimodal Federated Learning: A Survey through the Lens of Different FL Paradigms
por: Peng, Yuanzhe, et al.
Publicado: (2025)
por: Peng, Yuanzhe, et al.
Publicado: (2025)
Graph Feature Preprocessor: Real-time Subgraph-based Feature Extraction for Financial Crime Detection
por: Blanuša, Jovan, et al.
Publicado: (2024)
por: Blanuša, Jovan, et al.
Publicado: (2024)
NeFL: Nested Model Scaling for Federated Learning with System Heterogeneous Clients
por: Kang, Honggu, et al.
Publicado: (2023)
por: Kang, Honggu, et al.
Publicado: (2023)
ALIGN-FL: Architecture-independent Learning through Invariant Generative component sharing in Federated Learning
por: Gulati, Mayank, et al.
Publicado: (2025)
por: Gulati, Mayank, et al.
Publicado: (2025)
OledFL: Unleashing the Potential of Decentralized Federated Learning via Opposite Lookahead Enhancement
por: Li, Qinglun, et al.
Publicado: (2024)
por: Li, Qinglun, et al.
Publicado: (2024)
CDKT-FL: Cross-Device Knowledge Transfer using Proxy Dataset in Federated Learning
por: Le, Huy Q., et al.
Publicado: (2022)
por: Le, Huy Q., et al.
Publicado: (2022)
DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices
por: Jia, Yongzhe, et al.
Publicado: (2024)
por: Jia, Yongzhe, et al.
Publicado: (2024)
SacFL: Self-Adaptive Federated Continual Learning for Resource-Constrained End Devices
por: Zhong, Zhengyi, et al.
Publicado: (2025)
por: Zhong, Zhengyi, et al.
Publicado: (2025)
BadPromptFL: A Novel Backdoor Threat to Prompt-based Federated Learning in Multimodal Models
por: Zhang, Maozhen, et al.
Publicado: (2025)
por: Zhang, Maozhen, et al.
Publicado: (2025)
FL-GUARD: A Holistic Framework for Run-Time Detection and Recovery of Negative Federated Learning
por: Lin, Hong, et al.
Publicado: (2024)
por: Lin, Hong, et al.
Publicado: (2024)
AugFL: Augmenting Federated Learning with Pretrained Models
por: Yue, Sheng, et al.
Publicado: (2025)
por: Yue, Sheng, et al.
Publicado: (2025)
VARS-FL: Validation-Aligned Client Selection for Non-IID Federated Learning in IoT Systems
por: Lakas, Mohamed, et al.
Publicado: (2026)
por: Lakas, Mohamed, et al.
Publicado: (2026)
QuantFL: Sustainable Federated Learning for Edge IoT via Pre-Trained Model Quantisation
por: Herath, Charuka, et al.
Publicado: (2026)
por: Herath, Charuka, et al.
Publicado: (2026)
A Privacy-Preserving Federated Framework with Hybrid Quantum-Enhanced Learning for Financial Fraud Detection
por: Sawaika, Abhishek, et al.
Publicado: (2025)
por: Sawaika, Abhishek, et al.
Publicado: (2025)
zkFL: Zero-Knowledge Proof-based Gradient Aggregation for Federated Learning
por: Wang, Zhipeng, et al.
Publicado: (2023)
por: Wang, Zhipeng, et al.
Publicado: (2023)
FL-TAC: Enhanced Fine-Tuning in Federated Learning via Low-Rank, Task-Specific Adapter Clustering
por: Ping, Siqi, et al.
Publicado: (2024)
por: Ping, Siqi, et al.
Publicado: (2024)
MLLM-LLaVA-FL: Multimodal Large Language Model Assisted Federated Learning
por: Zhang, Jianyi, et al.
Publicado: (2024)
por: Zhang, Jianyi, et al.
Publicado: (2024)
EAB-FL: Exacerbating Algorithmic Bias through Model Poisoning Attacks in Federated Learning
por: Meerza, Syed Irfan Ali, et al.
Publicado: (2024)
por: Meerza, Syed Irfan Ali, et al.
Publicado: (2024)
FilFL: Client Filtering for Optimized Client Participation in Federated Learning
por: Fourati, Fares, et al.
Publicado: (2023)
por: Fourati, Fares, et al.
Publicado: (2023)
Fin-Fed-OD: Federated Outlier Detection on Financial Tabular Data
por: Herurkar, Dayananda, et al.
Publicado: (2024)
por: Herurkar, Dayananda, et al.
Publicado: (2024)
CoLiDR: Concept Learning using Aggregated Disentangled Representations
por: Sinha, Sanchit, et al.
Publicado: (2024)
por: Sinha, Sanchit, et al.
Publicado: (2024)
Hybrid Federated and Split Learning for Privacy Preserving Clinical Prediction and Treatment Optimization
por: Akter, Farzana, et al.
Publicado: (2026)
por: Akter, Farzana, et al.
Publicado: (2026)
ProtoNAM: Prototypical Neural Additive Models for Interpretable Deep Tabular Learning
por: Xiong, Guangzhi, et al.
Publicado: (2024)
por: Xiong, Guangzhi, et al.
Publicado: (2024)
LoLaFL: Low-Latency Federated Learning via Forward-only Propagation
por: Zhang, Jierui, et al.
Publicado: (2024)
por: Zhang, Jierui, et al.
Publicado: (2024)
Enhancing Learning Path Recommendation via Multi-task Learning
por: Nasrin, Afsana, et al.
Publicado: (2025)
por: Nasrin, Afsana, et al.
Publicado: (2025)
DP2FL: Dual Prompt Personalized Federated Learning in Foundation Models
por: Chang, Ying, et al.
Publicado: (2025)
por: Chang, Ying, et al.
Publicado: (2025)
XAI-SOH-FL: Enhancing SOH-FL with Adaptive Aggregation and Explainable AI for Intrusion Detection in Heterogeneous IoT
por: Aslam, Ambreen, et al.
Publicado: (2026)
por: Aslam, Ambreen, et al.
Publicado: (2026)
CrimeAlarm: Towards Intensive Intent Dynamics in Fine-grained Crime Prediction
por: Hu, Kaixi, et al.
Publicado: (2024)
por: Hu, Kaixi, et al.
Publicado: (2024)
Crime Forecasting: A Spatio-temporal Analysis with Deep Learning Models
por: Mao, Li, et al.
Publicado: (2025)
por: Mao, Li, et al.
Publicado: (2025)
SpaFL: Communication-Efficient Federated Learning with Sparse Models and Low computational Overhead
por: Kim, Minsu, et al.
Publicado: (2024)
por: Kim, Minsu, et al.
Publicado: (2024)
An Efficient Unsupervised Federated Learning Approach for Anomaly Detection in Heterogeneous IoT Networks
por: Tajgardan, Mohsen, et al.
Publicado: (2026)
por: Tajgardan, Mohsen, et al.
Publicado: (2026)
Predicting the Lifespan of Industrial Printheads with Survival Analysis
por: Parii, Dan, et al.
Publicado: (2025)
por: Parii, Dan, et al.
Publicado: (2025)
Ejemplares similares
-
VFL-RPS: Relevant Participant Selection in Vertical Federated Learning
por: Khan, Afsana, et al.
Publicado: (2025) -
Incentive Allocation in Vertical Federated Learning Based on Bankruptcy Problem
por: Khan, Afsana, et al.
Publicado: (2023) -
ZK-HybridFL: Zero-Knowledge Proof-Enhanced Hybrid Ledger for Federated Learning
por: Taherpour, Amirhossein, et al.
Publicado: (2026) -
IP-FL: Incentivized and Personalized Federated Learning
por: Khan, Ahmad Faraz, et al.
Publicado: (2023) -
Empirical Capacity Model for Self-Attention Neural Networks
por: Härmä, Aki, et al.
Publicado: (2024)