CAFE: Carbon-Aware Federated Learning in Geographically Distributed Data Centers
Fuente:
arXiv
Saved in:
| Main Authors: | Bian, Jieming, Wang, Lei, Ren, Shaolei, Xu, Jie |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Prioritizing Modalities: Flexible Importance Scheduling in Federated Multimodal Learning
by: Bian, Jieming, et al.
Published: (2024)
by: Bian, Jieming, et al.
Published: (2024)
Accelerating Hybrid Federated Learning Convergence under Partial Participation
by: Bian, Jieming, et al.
Published: (2023)
by: Bian, Jieming, et al.
Published: (2023)
Efficient Data Distribution Estimation for Accelerated Federated Learning
by: Wang, Yuanli, et al.
Published: (2024)
by: Wang, Yuanli, et al.
Published: (2024)
FairGFL: Privacy-Preserving Fairness-Aware Federated Learning with Overlapping Subgraphs
by: Zhou, Zihao, et al.
Published: (2025)
by: Zhou, Zihao, et al.
Published: (2025)
Flexible Clustered Federated Learning for Client-Level Data Distribution Shift
by: Duan, Moming, et al.
Published: (2021)
by: Duan, Moming, et al.
Published: (2021)
SMoFi: Step-wise Momentum Fusion for Split Federated Learning on Heterogeneous Data
by: Yang, Mingkun, et al.
Published: (2025)
by: Yang, Mingkun, et al.
Published: (2025)
An Efficient Subspace Algorithm for Federated Learning on Heterogeneous Data
by: Zhang, Jiaojiao, et al.
Published: (2025)
by: Zhang, Jiaojiao, et al.
Published: (2025)
Review of Mathematical Optimization in Federated Learning
by: Yang, Shusen, et al.
Published: (2024)
by: Yang, Shusen, et al.
Published: (2024)
Efficient Federated Learning against Byzantine Attacks and Data Heterogeneity via Aggregating Normalized Gradients
by: Zuo, Shiyuan, et al.
Published: (2024)
by: Zuo, Shiyuan, et al.
Published: (2024)
FedMM: Federated Multi-Modal Learning with Modality Heterogeneity in Computational Pathology
by: Peng, Yuanzhe, et al.
Published: (2024)
by: Peng, Yuanzhe, et al.
Published: (2024)
EcoLearn: Optimizing the Carbon Footprint of Federated Learning
by: Mehboob, Talha, et al.
Published: (2023)
by: Mehboob, Talha, et al.
Published: (2023)
D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data
by: Marisetty, Harsha Varun, et al.
Published: (2025)
by: Marisetty, Harsha Varun, et al.
Published: (2025)
SemiSFL: Split Federated Learning on Unlabeled and Non-IID Data
by: Xu, Yang, et al.
Published: (2023)
by: Xu, Yang, et al.
Published: (2023)
Energy-Efficient Federated Learning for Edge Real-Time Vision via Joint Data, Computation, and Communication Design
by: Hou, Xiangwang, et al.
Published: (2025)
by: Hou, Xiangwang, et al.
Published: (2025)
An Efficient Gradient-Aware Error-Bounded Lossy Compressor for Federated Learning
by: Ye, Zhijing, et al.
Published: (2025)
by: Ye, Zhijing, et al.
Published: (2025)
Accelerating Wireless Distributed Learning via Hybrid Split and Federated Learning Optimization
by: Guo, Kun, et al.
Published: (2025)
by: Guo, Kun, et al.
Published: (2025)
Stragglers Can Contribute More: Uncertainty-Aware Distillation for Asynchronous Federated Learning
by: Wang, Yujia, et al.
Published: (2025)
by: Wang, Yujia, et al.
Published: (2025)
Drift-Aware Federated Learning: A Causal Perspective
by: Fang, Yunjie, et al.
Published: (2025)
by: Fang, Yunjie, et al.
Published: (2025)
pFedMoE: Data-Level Personalization with Mixture of Experts for Model-Heterogeneous Personalized Federated Learning
by: Yi, Liping, et al.
Published: (2024)
by: Yi, Liping, et al.
Published: (2024)
Bandwidth-Aware and Overlap-Weighted Compression for Communication-Efficient Federated Learning
by: Tang, Zichen, et al.
Published: (2024)
by: Tang, Zichen, et al.
Published: (2024)
Federated Model Heterogeneous Matryoshka Representation Learning
by: Yi, Liping, et al.
Published: (2024)
by: Yi, Liping, et al.
Published: (2024)
CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated Active Learning
by: Zhang, Jun, et al.
Published: (2025)
by: Zhang, Jun, et al.
Published: (2025)
Empowering Data Mesh with Federated Learning
by: Li, Haoyuan, et al.
Published: (2024)
by: Li, Haoyuan, et al.
Published: (2024)
FLMarket: Enabling Privacy-preserved Pre-training Data Pricing for Federated Learning
by: Wen, Zhenyu, et al.
Published: (2024)
by: Wen, Zhenyu, et al.
Published: (2024)
Heterogeneity-Aware Client Selection Methodology For Efficient Federated Learning
by: Balivada, Nihal, et al.
Published: (2026)
by: Balivada, Nihal, et al.
Published: (2026)
Maverick-Aware Shapley Valuation for Client Selection in Federated Learning
by: Yang, Mengwei, et al.
Published: (2024)
by: Yang, Mengwei, et al.
Published: (2024)
Resource-Aware Aggregation and Sparsification in Heterogeneous Ensemble Federated Learning
by: Ryum, Keumseo, et al.
Published: (2025)
by: Ryum, Keumseo, et al.
Published: (2025)
Lightweight Federated Learning over Wireless Edge Networks
by: Hou, Xiangwang, et al.
Published: (2025)
by: Hou, Xiangwang, et al.
Published: (2025)
NEST: Network- and Memory-Aware Device Placement For Distributed Deep Learning
by: Wang, Irene, et al.
Published: (2026)
by: Wang, Irene, et al.
Published: (2026)
Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data
by: Chen, Yiyue, et al.
Published: (2025)
by: Chen, Yiyue, et al.
Published: (2025)
Tackling Intertwined Data and Device Heterogeneities in Federated Learning with Unlimited Staleness
by: Wang, Haoming, et al.
Published: (2023)
by: Wang, Haoming, et al.
Published: (2023)
Neighborhood and Global Perturbations Supported SAM in Federated Learning: From Local Tweaks To Global Awareness
by: Li, Boyuan, et al.
Published: (2024)
by: Li, Boyuan, et al.
Published: (2024)
Towards Trustworthy Federated Learning
by: Basharat, Alina, et al.
Published: (2025)
by: Basharat, Alina, et al.
Published: (2025)
Virtual Nodes Can Help: Tackling Distribution Shifts in Federated Graph Learning
by: Fu, Xingbo, et al.
Published: (2024)
by: Fu, Xingbo, et al.
Published: (2024)
Federated Learning with Bilateral Curation for Partially Class-Disjoint Data
by: Fan, Ziqing, et al.
Published: (2024)
by: Fan, Ziqing, et al.
Published: (2024)
The Diversity Bonus: Learning from Dissimilar Distributed Clients in Personalized Federated Learning
by: Wu, Xinghao, et al.
Published: (2024)
by: Wu, Xinghao, et al.
Published: (2024)
Analytic Personalized Federated Meta-Learning
by: Gu, Shunxian, et al.
Published: (2025)
by: Gu, Shunxian, et al.
Published: (2025)
SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning
by: Li, Nan, et al.
Published: (2025)
by: Li, Nan, et al.
Published: (2025)
Communication and Computation Efficient Split Federated Learning in O-RAN
by: Gu, Shunxian, et al.
Published: (2025)
by: Gu, Shunxian, et al.
Published: (2025)
FedClust: Optimizing Federated Learning on Non-IID Data through Weight-Driven Client Clustering
by: Islam, Md Sirajul, et al.
Published: (2024)
by: Islam, Md Sirajul, et al.
Published: (2024)
Similar Items
-
Prioritizing Modalities: Flexible Importance Scheduling in Federated Multimodal Learning
by: Bian, Jieming, et al.
Published: (2024) -
Accelerating Hybrid Federated Learning Convergence under Partial Participation
by: Bian, Jieming, et al.
Published: (2023) -
Efficient Data Distribution Estimation for Accelerated Federated Learning
by: Wang, Yuanli, et al.
Published: (2024) -
FairGFL: Privacy-Preserving Fairness-Aware Federated Learning with Overlapping Subgraphs
by: Zhou, Zihao, et al.
Published: (2025) -
Flexible Clustered Federated Learning for Client-Level Data Distribution Shift
by: Duan, Moming, et al.
Published: (2021)