Eco-Friendly AI: Unleashing Data Power for Green Federated Learning
Fuente:
arXiv
Saved in:
| Main Authors: | Sabella, Mattia, Vitali, Monica |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
The Built-In Robustness of Decentralized Federated Averaging to Bad Data
by: Sabella, Samuele, et al.
Published: (2025)
by: Sabella, Samuele, et al.
Published: (2025)
Orchestrating Agents and Data for Enterprise: A Blueprint Architecture for Compound AI
by: Kandogan, Eser, et al.
Published: (2025)
by: Kandogan, Eser, et al.
Published: (2025)
Modyn: Data-Centric Machine Learning Pipeline Orchestration
by: Böther, Maximilian, et al.
Published: (2023)
by: Böther, Maximilian, et al.
Published: (2023)
GetBatch: Distributed Multi-Object Retrieval for ML Data Loading
by: Aizman, Alex, et al.
Published: (2026)
by: Aizman, Alex, et al.
Published: (2026)
FedRA: A Random Allocation Strategy for Federated Tuning to Unleash the Power of Heterogeneous Clients
by: Su, Shangchao, et al.
Published: (2023)
by: Su, Shangchao, et al.
Published: (2023)
On-device Online Learning and Semantic Management of TinyML Systems
by: Ren, Haoyu, et al.
Published: (2024)
by: Ren, Haoyu, 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)
Enabling Secure and Ephemeral AI Workloads in Data Mesh Environments
by: Patel, Chinkit, et al.
Published: (2025)
by: Patel, Chinkit, et al.
Published: (2025)
TablePuppet: A Generic Framework for Relational Federated Learning
by: Xu, Lijie, et al.
Published: (2024)
by: Xu, Lijie, et al.
Published: (2024)
FedGreen: Carbon-aware Federated Learning with Model Size Adaptation
by: Abbasi, Ali, et al.
Published: (2024)
by: Abbasi, Ali, et al.
Published: (2024)
ARL-Tangram: Unleash the Resource Efficiency in Agentic Reinforcement Learning
by: Xiao, Bangjun, et al.
Published: (2026)
by: Xiao, Bangjun, et al.
Published: (2026)
CommunityAI: Towards Community-based Federated Learning
by: Murturi, Ilir, et al.
Published: (2023)
by: Murturi, Ilir, et al.
Published: (2023)
PNCS:Power-Norm Cosine Similarity for Diverse Client Selection in Federated Learning
by: Li, Liangyan, et al.
Published: (2025)
by: Li, Liangyan, et al.
Published: (2025)
Snowpark: Performant, Secure, User-Friendly Data Engineering and AI/ML Next To Your Data
by: Baker, Brandon, et al.
Published: (2025)
by: Baker, Brandon, et al.
Published: (2025)
Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout
by: Liu, Ji, et al.
Published: (2025)
by: Liu, Ji, et al.
Published: (2025)
Learn How to Query from Unlabeled Data Streams in Federated Learning
by: Sun, Yuchang, et al.
Published: (2024)
by: Sun, Yuchang, et al.
Published: (2024)
Stable Diffusion-based Data Augmentation for Federated Learning with Non-IID Data
by: Morafah, Mahdi, et al.
Published: (2024)
by: Morafah, Mahdi, et al.
Published: (2024)
LCFed: An Efficient Clustered Federated Learning Framework for Heterogeneous Data
by: Zhang, Yuxin, et al.
Published: (2025)
by: Zhang, Yuxin, et al.
Published: (2025)
A Green Multi-Attribute Client Selection for Over-The-Air Federated Learning: A Grey-Wolf-Optimizer Approach
by: Driss, Maryam Ben, et al.
Published: (2024)
by: Driss, Maryam Ben, et al.
Published: (2024)
Personalizing Federated Learning for Hierarchical Edge Networks with Non-IID Data
by: Lee, Seunghyun, et al.
Published: (2025)
by: Lee, Seunghyun, et al.
Published: (2025)
FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Data
by: Zhang, Yuxin, et al.
Published: (2024)
by: Zhang, Yuxin, et al.
Published: (2024)
PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection
by: Xu, Ronghui, et al.
Published: (2024)
by: Xu, Ronghui, et al.
Published: (2024)
Data-Free Client Contribution Estimation via Logit Maximization for Federated Learning
by: Ukaye, Asim, et al.
Published: (2026)
by: Ukaye, Asim, et al.
Published: (2026)
PixelsDB: Serverless and NL-Aided Data Analytics with Flexible Service Levels and Prices
by: Bian, Haoqiong, et al.
Published: (2024)
by: Bian, Haoqiong, et al.
Published: (2024)
Building a Correct-by-Design Lakehouse. Data Contracts, Versioning, and Transactional Pipelines for Humans and Agents
by: Sheng, Weiming, et al.
Published: (2026)
by: Sheng, Weiming, et al.
Published: (2026)
FedFiTS: Fitness-Selected, Slotted Client Scheduling for Trustworthy Federated Learning in Healthcare AI
by: Kahenga, Ferdinand, et al.
Published: (2025)
by: Kahenga, Ferdinand, et al.
Published: (2025)
Full Scaling Automation for Sustainable Development of Green Data Centers
by: Wang, Shiyu, et al.
Published: (2023)
by: Wang, Shiyu, et al.
Published: (2023)
Compare Where It Matters: Using Layer-Wise Regularization To Improve Federated Learning on Heterogeneous Data
by: Son, Ha Min, et al.
Published: (2021)
by: Son, Ha Min, et al.
Published: (2021)
FedDAG: Clustered Federated Learning via Global Data and Gradient Integration for Heterogeneous Environments
by: Pramanik, Anik, et al.
Published: (2026)
by: Pramanik, Anik, et al.
Published: (2026)
FedClust: Tackling Data Heterogeneity in Federated Learning through Weight-Driven Client Clustering
by: Islam, Md Sirajul, et al.
Published: (2024)
by: Islam, Md Sirajul, et al.
Published: (2024)
Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data
by: Liu, Ji, et al.
Published: (2024)
by: Liu, Ji, et al.
Published: (2024)
Morphing-based Compression for Data-centric ML Pipelines
by: Baunsgaard, Sebastian, et al.
Published: (2025)
by: Baunsgaard, Sebastian, et al.
Published: (2025)
Federated Multi-Objective Learning
by: Yang, Haibo, et al.
Published: (2023)
by: Yang, Haibo, et al.
Published: (2023)
Federated Learning with Flexible Architectures
by: Park, Jong-Ik, et al.
Published: (2024)
by: Park, Jong-Ik, et al.
Published: (2024)
Federated Learning with Workload Reduction through Partial Training of Client Models and Entropy-Based Data Selection
by: Shi, Hongrui, et al.
Published: (2024)
by: Shi, Hongrui, et al.
Published: (2024)
FedLECC: Cluster- and Loss-Guided Client Selection for Federated Learning under Non-IID Data
by: Jimenez-Gutierrez, Daniel M., et al.
Published: (2026)
by: Jimenez-Gutierrez, Daniel M., et al.
Published: (2026)
TensAIR: Real-Time Training of Neural Networks from Data-streams
by: Tosi, Mauro D. L., et al.
Published: (2022)
by: Tosi, Mauro D. L., et al.
Published: (2022)
FedPBS: Proximal-Balanced Scaling Federated Learning Model for Robust Personalized Training for Non-IID Data
by: AbouNassar, Eman M., et al.
Published: (2026)
by: AbouNassar, Eman M., et al.
Published: (2026)
On Using Large-Batches in Federated Learning
by: Tyagi, Sahil
Published: (2025)
by: Tyagi, Sahil
Published: (2025)
Uncertainty-Aware Explainable Federated Learning
by: Zhang, Yanci, et al.
Published: (2025)
by: Zhang, Yanci, et al.
Published: (2025)
Similar Items
-
The Built-In Robustness of Decentralized Federated Averaging to Bad Data
by: Sabella, Samuele, et al.
Published: (2025) -
Orchestrating Agents and Data for Enterprise: A Blueprint Architecture for Compound AI
by: Kandogan, Eser, et al.
Published: (2025) -
Modyn: Data-Centric Machine Learning Pipeline Orchestration
by: Böther, Maximilian, et al.
Published: (2023) -
GetBatch: Distributed Multi-Object Retrieval for ML Data Loading
by: Aizman, Alex, et al.
Published: (2026) -
FedRA: A Random Allocation Strategy for Federated Tuning to Unleash the Power of Heterogeneous Clients
by: Su, Shangchao, et al.
Published: (2023)