Holistic Evaluation Metrics: Use Case Sensitive Evaluation Metrics for Federated Learning
Fuente:
arXiv
Saved in:
| Main Authors: | Li, Yanli, Ibrahim, Jehad, Chen, Huaming, Yuan, Dong, Choo, Kim-Kwang Raymond |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
EASTER: Embedding Aggregation-based Heterogeneous Models Training in Vertical Federated Learning
by: Wang, Shuo, et al.
Published: (2023)
by: Wang, Shuo, et al.
Published: (2023)
Graph-Structured Deep Learning Framework for Multi-task Contention Identification with High-dimensional Metrics
by: Yang, Xiao, et al.
Published: (2026)
by: Yang, Xiao, et al.
Published: (2026)
FedDriveScore: Federated Scoring Driving Behavior with a Mixture of Metric Distributions
by: Lu, Lin
Published: (2024)
by: Lu, Lin
Published: (2024)
A Survey on Contribution Evaluation in Vertical Federated Learning
by: Cui, Yue, et al.
Published: (2024)
by: Cui, Yue, et al.
Published: (2024)
Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challenges
by: Li, Yanli, et al.
Published: (2024)
by: Li, Yanli, et al.
Published: (2024)
FLAM: Evaluating Model Performance with Aggregatable Measures in Federated Learning
by: Stricker, Fabian, et al.
Published: (2026)
by: Stricker, Fabian, et al.
Published: (2026)
Federated Graph Learning with Graphless Clients
by: Fu, Xingbo, et al.
Published: (2024)
by: Fu, Xingbo, et al.
Published: (2024)
Not All Federated Learning Algorithms Are Created Equal: A Performance Evaluation Study
by: Baumgart, Gustav A., et al.
Published: (2024)
by: Baumgart, Gustav A., et al.
Published: (2024)
A Robust Power Model Training Framework for Cloud Native Runtime Energy Metric Exporter
by: Choochotkaew, Sunyanan, et al.
Published: (2024)
by: Choochotkaew, Sunyanan, et al.
Published: (2024)
Robust Federated Learning with Global Sensitivity Estimation for Financial Risk Management
by: Zhao, Lei, et al.
Published: (2025)
by: Zhao, Lei, et al.
Published: (2025)
Secure Federated Learning Across Heterogeneous Cloud and High-Performance Computing Resources -- A Case Study on Federated Fine-tuning of LLaMA 2
by: Li, Zilinghan, et al.
Published: (2024)
by: Li, Zilinghan, et al.
Published: (2024)
Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not?
by: Zhao, Yi, et al.
Published: (2026)
by: Zhao, Yi, et al.
Published: (2026)
Communication-Efficient Multimodal Federated Learning: Joint Modality and Client Selection
by: Yuan, Liangqi, et al.
Published: (2024)
by: Yuan, Liangqi, et al.
Published: (2024)
Revisiting Early-Learning Regularization When Federated Learning Meets Noisy Labels
by: Kim, Taehyeon, et al.
Published: (2024)
by: Kim, Taehyeon, et al.
Published: (2024)
FL-GUARD: A Holistic Framework for Run-Time Detection and Recovery of Negative Federated Learning
by: Lin, Hong, et al.
Published: (2024)
by: Lin, Hong, et al.
Published: (2024)
FedHB: Hierarchical Bayesian Federated Learning
by: Kim, Minyoung, et al.
Published: (2023)
by: Kim, Minyoung, et al.
Published: (2023)
FedAgg: Adaptive Federated Learning with Aggregated Gradients
by: Yuan, Wenhao, et al.
Published: (2023)
by: Yuan, Wenhao, et al.
Published: (2023)
Using Diffusion Models as Generative Replay in Continual Federated Learning -- What will Happen?
by: Mei, Yongsheng, et al.
Published: (2024)
by: Mei, Yongsheng, et al.
Published: (2024)
Sketched Gaussian Mechanism for Private Federated Learning
by: Li, Qiaobo, et al.
Published: (2025)
by: Li, Qiaobo, et al.
Published: (2025)
GAS: Generative Activation-Aided Asynchronous Split Federated Learning
by: Yang, Jiarong, et al.
Published: (2024)
by: Yang, Jiarong, et al.
Published: (2024)
A Survey for Federated Learning Evaluations: Goals and Measures
by: Chai, Di, et al.
Published: (2023)
by: Chai, Di, et al.
Published: (2023)
An Efficient Subspace Algorithm for Federated Learning on Heterogeneous Data
by: Zhang, Jiaojiao, et al.
Published: (2025)
by: Zhang, Jiaojiao, et al.
Published: (2025)
Flexible Personalized Split Federated Learning for On-Device Fine-Tuning of Foundation Models
by: Yuan, Tianjun, et al.
Published: (2025)
by: Yuan, Tianjun, et al.
Published: (2025)
Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimization
by: Li, Zhe, et al.
Published: (2024)
by: Li, Zhe, et al.
Published: (2024)
FedQueue: Queue-Aware Federated Learning for Cross-Facility HPC Training
by: Li, Yijiang, et al.
Published: (2026)
by: Li, Yijiang, et al.
Published: (2026)
Hypernetworks for Model-Heterogeneous Personalized Federated Learning
by: Zhang, Chen, et al.
Published: (2025)
by: Zhang, Chen, et al.
Published: (2025)
Adaptive Federated Learning via New Entropy Approach
by: Zheng, Shensheng, et al.
Published: (2023)
by: Zheng, Shensheng, et al.
Published: (2023)
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)
Incentive-Compatible Federated Learning with Stackelberg Game Modeling
by: Javaherian, Simin, et al.
Published: (2025)
by: Javaherian, Simin, et al.
Published: (2025)
Navigating High-Degree Heterogeneity: Federated Learning in Aerial and Space Networks
by: Dong, Fan, et al.
Published: (2024)
by: Dong, Fan, et al.
Published: (2024)
HeteroSwitch: Characterizing and Taming System-Induced Data Heterogeneity in Federated Learning
by: Kim, Gyudong, et al.
Published: (2024)
by: Kim, Gyudong, et al.
Published: (2024)
Personalized Federated Learning via ADMM with Moreau Envelope
by: Zhu, Shengkun, et al.
Published: (2023)
by: Zhu, Shengkun, et al.
Published: (2023)
Beyond the Federation: Topology-aware Federated Learning for Generalization to Unseen Clients
by: Ma, Mengmeng, et al.
Published: (2024)
by: Ma, Mengmeng, et al.
Published: (2024)
Empowering Data Mesh with Federated Learning
by: Li, Haoyuan, et al.
Published: (2024)
by: Li, Haoyuan, et al.
Published: (2024)
Towards Client Driven Federated Learning
by: Li, Songze, et al.
Published: (2024)
by: Li, Songze, et al.
Published: (2024)
Robust Model Aggregation for Heterogeneous Federated Learning: Analysis and Optimizations
by: Shao, Yumeng, et al.
Published: (2024)
by: Shao, Yumeng, et al.
Published: (2024)
Vertical Federated Learning: Challenges, Methodologies and Experiments
by: Wei, Kang, et al.
Published: (2022)
by: Wei, Kang, et al.
Published: (2022)
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)
Towards Efficient Replay in Federated Incremental Learning
by: Li, Yichen, et al.
Published: (2024)
by: Li, Yichen, et al.
Published: (2024)
CSAFL: A Clustered Semi-Asynchronous Federated Learning Framework
by: Zhang, Yu, et al.
Published: (2021)
by: Zhang, Yu, et al.
Published: (2021)
Similar Items
-
EASTER: Embedding Aggregation-based Heterogeneous Models Training in Vertical Federated Learning
by: Wang, Shuo, et al.
Published: (2023) -
Graph-Structured Deep Learning Framework for Multi-task Contention Identification with High-dimensional Metrics
by: Yang, Xiao, et al.
Published: (2026) -
FedDriveScore: Federated Scoring Driving Behavior with a Mixture of Metric Distributions
by: Lu, Lin
Published: (2024) -
A Survey on Contribution Evaluation in Vertical Federated Learning
by: Cui, Yue, et al.
Published: (2024) -
Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challenges
by: Li, Yanli, et al.
Published: (2024)