Guardado en:
| Autores principales: | Zhang, Xianzhi, Zhou, Yipeng, Hu, Miao, Wu, Di, Liao, Pengshan, Guizani, Mohsen, Sheng, Michael |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2412.02934 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
PPVF: An Efficient Privacy-Preserving Online Video Fetching Framework with Correlated Differential Privacy
por: Zhang, Xianzhi, et al.
Publicado: (2024)
por: Zhang, Xianzhi, et al.
Publicado: (2024)
A Survey on Privacy-Preserving Caching at Network Edge: Classification, Solutions, and Challenges
por: Zhang, Xianzhi, et al.
Publicado: (2024)
por: Zhang, Xianzhi, et al.
Publicado: (2024)
Tackling Privacy Heterogeneity in Differentially Private Federated Learning
por: Xu, Ruichen, et al.
Publicado: (2026)
por: Xu, Ruichen, et al.
Publicado: (2026)
Federated Learning Using Coupled Tensor Train Decomposition
por: Zhang, Xiangtao, et al.
Publicado: (2024)
por: Zhang, Xiangtao, et al.
Publicado: (2024)
Resource Allocation of Industry 4.0 Micro-Service Applications across Serverless Fog Federation
por: Hussain, Razin Farhan, et al.
Publicado: (2024)
por: Hussain, Razin Farhan, et al.
Publicado: (2024)
Federated Learning and Evolutionary Game Model for Fog Federation Formation
por: Yasser, Zyad, et al.
Publicado: (2024)
por: Yasser, Zyad, et al.
Publicado: (2024)
Skipper: Maximal Matching with a Single Pass over Edges
por: Esfahani, Mohsen Koohi
Publicado: (2025)
por: Esfahani, Mohsen Koohi
Publicado: (2025)
Differentially Private Federated Learning With Time-Adaptive Privacy Spending
por: Kiani, Shahrzad, et al.
Publicado: (2025)
por: Kiani, Shahrzad, et al.
Publicado: (2025)
One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning
por: Lan, Muhang, et al.
Publicado: (2025)
por: Lan, Muhang, et al.
Publicado: (2025)
BOA Constrictor: Squeezing Performance out of GPUs in the Cloud via Budget-Optimal Allocation
por: Li, Zhouzi, et al.
Publicado: (2026)
por: Li, Zhouzi, et al.
Publicado: (2026)
Locally Differentially Private Online Federated Learning With Correlated Noise
por: Zhang, Jiaojiao, et al.
Publicado: (2024)
por: Zhang, Jiaojiao, et al.
Publicado: (2024)
Lightweight Federated Learning with Differential Privacy and Straggler Resilience
por: Hong, Shu, et al.
Publicado: (2024)
por: Hong, Shu, et al.
Publicado: (2024)
HE2C: A Holistic Approach for Allocating Latency-Sensitive AI Tasks across Edge-Cloud
por: Kim, Minseo, et al.
Publicado: (2024)
por: Kim, Minseo, et al.
Publicado: (2024)
Communication-and-Computation Efficient Split Federated Learning: Gradient Aggregation and Resource Management
por: Liang, Yipeng, et al.
Publicado: (2025)
por: Liang, Yipeng, et al.
Publicado: (2025)
Federated Learning within Global Energy Budget over Heterogeneous Edge Accelerators
por: Banerjee, Roopkatha, et al.
Publicado: (2025)
por: Banerjee, Roopkatha, et al.
Publicado: (2025)
Differentially Private Clustered Federated Learning
por: Malekmohammadi, Saber, et al.
Publicado: (2024)
por: Malekmohammadi, Saber, et al.
Publicado: (2024)
Energy-efficient Federated Learning with Dynamic Model Size Allocation
por: Kumar, M S Chaitanya, et al.
Publicado: (2024)
por: Kumar, M S Chaitanya, et al.
Publicado: (2024)
Adversarial Analysis of the Differentially-Private Federated Learning in Cyber-Physical Critical Infrastructures
por: Hossain, Md Tamjid, et al.
Publicado: (2022)
por: Hossain, Md Tamjid, et al.
Publicado: (2022)
Differential Privacy Preserving Distributed Quantum Computing
por: Zhong, Hui, et al.
Publicado: (2024)
por: Zhong, Hui, et al.
Publicado: (2024)
Parallel Dynamic Maximal Matching
por: Ghaffari, Mohsen, et al.
Publicado: (2024)
por: Ghaffari, Mohsen, et al.
Publicado: (2024)
Towards Privacy-, Budget-, and Deadline-Aware Service Optimization for Large Medical Image Processing across Hybrid Clouds
por: Wang, Yuandou, et al.
Publicado: (2024)
por: Wang, Yuandou, et al.
Publicado: (2024)
Optimizing Resource Allocation and Energy Efficiency in Federated Fog Computing for IoT
por: Shah, Syed Sarmad, et al.
Publicado: (2025)
por: Shah, Syed Sarmad, et al.
Publicado: (2025)
Decentralized Proactive Model Offloading and Resource Allocation for Split and Federated Learning
por: Huang, Binbin, et al.
Publicado: (2024)
por: Huang, Binbin, et al.
Publicado: (2024)
Differentially-Private Multi-Tier Federated Learning
por: Chen, Evan, et al.
Publicado: (2024)
por: Chen, Evan, et al.
Publicado: (2024)
Generative Federated Learning for Smart Prediction and Recommendation Applications
por: Mukherjee, Anwesha, et al.
Publicado: (2025)
por: Mukherjee, Anwesha, et al.
Publicado: (2025)
Multi-Hop Privacy Propagation for Differentially Private Federated Learning in Social Networks
por: Lin, Chenchen, et al.
Publicado: (2025)
por: Lin, Chenchen, et al.
Publicado: (2025)
Federated Learning with Integrated Sensing, Communication, and Computation: Frameworks and Performance Analysis
por: Liang, Yipeng, et al.
Publicado: (2024)
por: Liang, Yipeng, et al.
Publicado: (2024)
FedPDD: A Privacy-preserving Double Distillation Framework for Cross-silo Federated Recommendation
por: Wan, Sheng, et al.
Publicado: (2023)
por: Wan, Sheng, et al.
Publicado: (2023)
ParaAegis: Parallel Protection for Flexible Privacy-preserved Federated Learning
por: Wu, Zihou, et al.
Publicado: (2025)
por: Wu, Zihou, et al.
Publicado: (2025)
Differentially Private Perturbed Push-Sum Protocol and Its Application in Non-Convex Optimization
por: Zhou, Yiming, et al.
Publicado: (2026)
por: Zhou, Yiming, et al.
Publicado: (2026)
Optimizing Frequent Checkpointing via Low-Cost Differential for Distributed Training Systems
por: Yao, Chenxuan, et al.
Publicado: (2025)
por: Yao, Chenxuan, et al.
Publicado: (2025)
DPBalance: Efficient and Fair Privacy Budget Scheduling for Federated Learning as a Service
por: Liu, Yu, et al.
Publicado: (2024)
por: Liu, Yu, et al.
Publicado: (2024)
FedSem: A Resource Allocation Scheme for Federated Learning Assisted Semantic Communication
por: Zhou, Xinyu, et al.
Publicado: (2025)
por: Zhou, Xinyu, et al.
Publicado: (2025)
Dynamic Client Clustering, Bandwidth Allocation, and Workload Optimization for Semi-synchronous Federated Learning
por: Yu, Liangkun, et al.
Publicado: (2024)
por: Yu, Liangkun, et al.
Publicado: (2024)
Age Aware Scheduling for Differentially-Private Federated Learning
por: Lin, Kuan-Yu, et al.
Publicado: (2024)
por: Lin, Kuan-Yu, et al.
Publicado: (2024)
Differentially Private Online Federated Learning with Correlated Noise
por: Zhang, Jiaojiao, et al.
Publicado: (2024)
por: Zhang, Jiaojiao, et al.
Publicado: (2024)
ExClique: An Express Consensus Algorithm for High-Speed Transaction Process in Blockchains
por: Zhao, Chonghe, et al.
Publicado: (2025)
por: Zhao, Chonghe, et al.
Publicado: (2025)
FLMarket: Enabling Privacy-preserved Pre-training Data Pricing for Federated Learning
por: Wen, Zhenyu, et al.
Publicado: (2024)
por: Wen, Zhenyu, et al.
Publicado: (2024)
Privacy-Preserving Federated Heavy Hitter Analytics for Non-IID Data
por: Shao, Jiaqi, et al.
Publicado: (2023)
por: Shao, Jiaqi, et al.
Publicado: (2023)
Comments on "Federated Learning with Differential Privacy: Algorithms and Performance Analysis"
por: Talaei, Mahtab, et al.
Publicado: (2024)
por: Talaei, Mahtab, et al.
Publicado: (2024)
Ejemplares similares
-
PPVF: An Efficient Privacy-Preserving Online Video Fetching Framework with Correlated Differential Privacy
por: Zhang, Xianzhi, et al.
Publicado: (2024) -
A Survey on Privacy-Preserving Caching at Network Edge: Classification, Solutions, and Challenges
por: Zhang, Xianzhi, et al.
Publicado: (2024) -
Tackling Privacy Heterogeneity in Differentially Private Federated Learning
por: Xu, Ruichen, et al.
Publicado: (2026) -
Federated Learning Using Coupled Tensor Train Decomposition
por: Zhang, Xiangtao, et al.
Publicado: (2024) -
Resource Allocation of Industry 4.0 Micro-Service Applications across Serverless Fog Federation
por: Hussain, Razin Farhan, et al.
Publicado: (2024)