Privacy Protection in Prosumer Energy Management Based on Federated Learning
Fuente:
arXiv
Saved in:
| Main Authors: | Li, Yunfeng, Li, Xiaolin Li Zhitao, Li, Gangqiang |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Free Privacy Protection for Wireless Federated Learning: Enjoy It or Suffer from It?
by: Li, Weicai, et al.
Published: (2025)
by: Li, Weicai, et al.
Published: (2025)
Deep Reinforcement Learning-Based Bidding Strategies for Prosumers Trading in Double Auction-Based Transactive Energy Market
by: Jiang, Jun, et al.
Published: (2025)
by: Jiang, Jun, et al.
Published: (2025)
ParaAegis: Parallel Protection for Flexible Privacy-preserved Federated Learning
by: Wu, Zihou, et al.
Published: (2025)
by: Wu, Zihou, et al.
Published: (2025)
Federated Learning via Variational Bayesian Inference: Personalization, Sparsity and Clustering
by: Zhang, Xu, et al.
Published: (2023)
by: Zhang, Xu, et al.
Published: (2023)
WassFFed: Wasserstein Fair Federated Learning
by: Han, Zhongxuan, et al.
Published: (2024)
by: Han, Zhongxuan, et al.
Published: (2024)
A New Perspective on Privacy Protection in Federated Learning with Granular-Ball Computing
by: Lai, Guannan, et al.
Published: (2025)
by: Lai, Guannan, et al.
Published: (2025)
Balancing Privacy-Quality-Efficiency in Federated Learning through Round-Based Interleaving of Protection Techniques
by: Wang, Yenan, et al.
Published: (2026)
by: Wang, Yenan, et al.
Published: (2026)
Lossless Privacy-Preserving Aggregation for Decentralized Federated Learning
by: Miao, Xiaoye, et al.
Published: (2025)
by: Miao, Xiaoye, et al.
Published: (2025)
A Privacy-Preserving Framework for Advertising Personalization Incorporating Federated Learning and Differential Privacy
by: Li, Xiang, et al.
Published: (2025)
by: Li, Xiang, et al.
Published: (2025)
High-Energy Concentration for Federated Learning in Frequency Domain
by: Shi, Haozhi, et al.
Published: (2025)
by: Shi, Haozhi, et al.
Published: (2025)
Convergent Differential Privacy Analysis for General Federated Learning
by: Sun, Yan, et al.
Published: (2024)
by: Sun, Yan, et al.
Published: (2024)
Improving LoRA in Privacy-preserving Federated Learning
by: Sun, Youbang, et al.
Published: (2024)
by: Sun, Youbang, et al.
Published: (2024)
Federated Multi-Agent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multi-Microgrid Energy Management
by: Li, Yuanzheng, et al.
Published: (2022)
by: Li, Yuanzheng, et al.
Published: (2022)
Privacy-Preserving Federated Learning via Dataset Distillation
by: Xu, ShiMao, et al.
Published: (2024)
by: Xu, ShiMao, et al.
Published: (2024)
Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMM
by: Xie, Chulin, et al.
Published: (2022)
by: Xie, Chulin, et al.
Published: (2022)
On the Privacy Effect of Data Enhancement via the Lens of Memorization
by: Li, Xiao, et al.
Published: (2022)
by: Li, Xiao, et al.
Published: (2022)
Federated Transfer Learning with Differential Privacy
by: Li, Mengchu, et al.
Published: (2024)
by: Li, Mengchu, et al.
Published: (2024)
Clustered Federated Learning for Generalizable FDIA Detection in Smart Grids with Heterogeneous Data
by: Li, Yunfeng, et al.
Published: (2025)
by: Li, Yunfeng, et al.
Published: (2025)
MAP: Model Aggregation and Personalization in Federated Learning with Incomplete Classes
by: Li, Xin-Chun, et al.
Published: (2024)
by: Li, Xin-Chun, et al.
Published: (2024)
FedRE: Robust and Effective Federated Learning with Privacy Preference
by: Xiao, Tianzhe, et al.
Published: (2025)
by: Xiao, Tianzhe, et al.
Published: (2025)
Communication-Efficient and Privacy-Adaptable Mechanism for Federated Learning
by: Ling, Chih Wei, et al.
Published: (2025)
by: Ling, Chih Wei, et al.
Published: (2025)
PQFed: A Privacy-Preserving Quality-Controlled Federated Learning Framework
by: Yue, Weiqi, et al.
Published: (2025)
by: Yue, Weiqi, et al.
Published: (2025)
Federated Learning with Enhanced Privacy via Model Splitting and Random Client Participation
by: Li, Yiwei, et al.
Published: (2025)
by: Li, Yiwei, et al.
Published: (2025)
FedHybrid: Breaking the Memory Wall of Federated Learning via Hybrid Tensor Management
by: Tam, Kahou, et al.
Published: (2025)
by: Tam, Kahou, et al.
Published: (2025)
AI-Powered Predictions for Electricity Load in Prosumer Communities
by: Kychkin, Aleksei, et al.
Published: (2024)
by: Kychkin, Aleksei, et al.
Published: (2024)
Adaptive Coded Federated Learning: Privacy Preservation and Straggler Mitigation
by: Li, Chengxi, et al.
Published: (2024)
by: Li, Chengxi, et al.
Published: (2024)
FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning
by: Wu, Yuezhou, et al.
Published: (2021)
by: Wu, Yuezhou, et al.
Published: (2021)
Graph Federated Learning for Personalized Privacy Recommendation
by: Na, Ce, et al.
Published: (2025)
by: Na, Ce, et al.
Published: (2025)
On Privacy-Preserving Image Transmission in Low-Altitude Networks: A Swin Transformer-Based Framework with Federated Learning
by: Zhang, Kexin, et al.
Published: (2026)
by: Zhang, Kexin, et al.
Published: (2026)
The Power of Bias: Optimizing Client Selection in Federated Learning with Heterogeneous Differential Privacy
by: Ma, Jiating, et al.
Published: (2024)
by: Ma, Jiating, et al.
Published: (2024)
Adaptive Decentralized Federated Learning in Energy and Latency Constrained Wireless Networks
by: Yan, Zhigang, et al.
Published: (2024)
by: Yan, Zhigang, et al.
Published: (2024)
Position Paper: Assessing Robustness, Privacy, and Fairness in Federated Learning Integrated with Foundation Models
by: Wang, Jiaqi, et al.
Published: (2024)
by: Wang, Jiaqi, et al.
Published: (2024)
Social-Aware Clustered Federated Learning with Customized Privacy Preservation
by: Wang, Yuntao, et al.
Published: (2022)
by: Wang, Yuntao, et al.
Published: (2022)
Fast, Private, and Protected: Safeguarding Data Privacy and Defending Against Model Poisoning Attacks in Federated Learning
by: Assumpcao, Nicolas Riccieri Gardin, et al.
Published: (2025)
by: Assumpcao, Nicolas Riccieri Gardin, et al.
Published: (2025)
Breaking Privacy in Federated Clustering: Perfect Input Reconstruction via Temporal Correlations
by: Yang, Guang, et al.
Published: (2025)
by: Yang, Guang, et al.
Published: (2025)
Rec-AD: An Efficient Computation Framework for FDIA Detection Based on Tensor Train Decomposition and Deep Learning Recommendation Model
by: Li, Yunfeng, et al.
Published: (2025)
by: Li, Yunfeng, et al.
Published: (2025)
Adaptive Backtracking for Privacy Protection in Large Language Models
by: Yao, Zhihao, et al.
Published: (2025)
by: Yao, Zhihao, et al.
Published: (2025)
ULDP-FL: Federated Learning with Across Silo User-Level Differential Privacy
by: Kato, Fumiyuki, et al.
Published: (2023)
by: Kato, Fumiyuki, et al.
Published: (2023)
Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy
by: You, Zhichao, et al.
Published: (2025)
by: You, Zhichao, et al.
Published: (2025)
Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks
by: Andong, Francisco Javier Esono Nkulu, et al.
Published: (2025)
by: Andong, Francisco Javier Esono Nkulu, et al.
Published: (2025)
Similar Items
-
Free Privacy Protection for Wireless Federated Learning: Enjoy It or Suffer from It?
by: Li, Weicai, et al.
Published: (2025) -
Deep Reinforcement Learning-Based Bidding Strategies for Prosumers Trading in Double Auction-Based Transactive Energy Market
by: Jiang, Jun, et al.
Published: (2025) -
ParaAegis: Parallel Protection for Flexible Privacy-preserved Federated Learning
by: Wu, Zihou, et al.
Published: (2025) -
Federated Learning via Variational Bayesian Inference: Personalization, Sparsity and Clustering
by: Zhang, Xu, et al.
Published: (2023) -
WassFFed: Wasserstein Fair Federated Learning
by: Han, Zhongxuan, et al.
Published: (2024)