Pursuing Overall Welfare in Federated Learning through Sequential Decision Making
Fuente:
arXiv
Saved in:
| Main Authors: | Hahn, Seok-Ju, Kim, Gi-Soo, Lee, Junghye |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Algorithms for Collaborative Machine Learning under Statistical Heterogeneity
by: Hahn, Seok-Ju
Published: (2024)
by: Hahn, Seok-Ju
Published: (2024)
Towards Seamless Hierarchical Federated Learning under Intermittent Client Participation: A Stagewise Decision-Making Methodology
by: Wu, Minghong, et al.
Published: (2025)
by: Wu, Minghong, et al.
Published: (2025)
BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoT
by: Ju, Zehao, et al.
Published: (2024)
by: Ju, Zehao, et al.
Published: (2024)
Corrected with the Latest Version: Make Robust Asynchronous Federated Learning Possible
by: Lu, Chaoyi, et al.
Published: (2025)
by: Lu, Chaoyi, et al.
Published: (2025)
Asynchronous Personalized Federated Learning through Global Memorization
by: Wan, Fan, et al.
Published: (2025)
by: Wan, Fan, et al.
Published: (2025)
Revisiting Early-Learning Regularization When Federated Learning Meets Noisy Labels
by: Kim, Taehyeon, et al.
Published: (2024)
by: Kim, Taehyeon, et al.
Published: (2024)
FedHB: Hierarchical Bayesian Federated Learning
by: Kim, Minyoung, et al.
Published: (2023)
by: Kim, Minyoung, et al.
Published: (2023)
Shabari: Delayed Decision-Making for Faster and Efficient Serverless Functions
by: Sinha, Prasoon, et al.
Published: (2024)
by: Sinha, Prasoon, et al.
Published: (2024)
Convergence Analysis of Federated Learning Methods Using Backward Error Analysis
by: Lim, Jinwoo, et al.
Published: (2025)
by: Lim, Jinwoo, et al.
Published: (2025)
Heterogeneous Federated Learning with Prototype Alignment and Upscaling
by: Lee, Gyuejeong, et al.
Published: (2025)
by: Lee, Gyuejeong, et al.
Published: (2025)
Look Back for More: Harnessing Historical Sequential Updates for Personalized Federated Adapter Tuning
by: Peng, Danni, et al.
Published: (2025)
by: Peng, Danni, et al.
Published: (2025)
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)
SyncFed: Time-Aware Federated Learning through Explicit Timestamping and Synchronization
by: Gül, Baran Can, et al.
Published: (2025)
by: Gül, Baran Can, et al.
Published: (2025)
Flame: Simplifying Topology Extension in Federated Learning
by: Daga, Harshit, et al.
Published: (2023)
by: Daga, Harshit, et al.
Published: (2023)
Communication-Efficient Federated Learning through Adaptive Weight Clustering and Server-Side Distillation
by: Tsouvalas, Vasileios, et al.
Published: (2024)
by: Tsouvalas, Vasileios, et al.
Published: (2024)
TurboSVM-FL: Boosting Federated Learning through SVM Aggregation for Lazy Clients
by: Wang, Mengdi, et al.
Published: (2024)
by: Wang, Mengdi, et al.
Published: (2024)
LIFL: A Lightweight, Event-driven Serverless Platform for Federated Learning
by: Qi, Shixiong, et al.
Published: (2024)
by: Qi, Shixiong, et al.
Published: (2024)
pMixFed: Efficient Personalized Federated Learning through Adaptive Layer-Wise Mixup
by: Saadati, Yasaman, et al.
Published: (2025)
by: Saadati, Yasaman, et al.
Published: (2025)
Buffer-based Gradient Projection for Continual Federated Learning
by: Dai, Shenghong, et al.
Published: (2024)
by: Dai, Shenghong, et al.
Published: (2024)
Robust Federated Finetuning of Foundation Models via Alternating Minimization of LoRA
by: Chen, Shuangyi, et al.
Published: (2024)
by: Chen, Shuangyi, et al.
Published: (2024)
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)
Partial Federated Learning
by: Feng, Tiantian, et al.
Published: (2024)
by: Feng, Tiantian, et al.
Published: (2024)
Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning
by: Ji, Shaoxiong, et al.
Published: (2021)
by: Ji, Shaoxiong, et al.
Published: (2021)
FRAIN to Train: A Fast-and-Reliable Solution for Decentralized Federated Learning
by: Park, Sanghyeon, et al.
Published: (2025)
by: Park, Sanghyeon, et al.
Published: (2025)
Holistic Evaluation Metrics: Use Case Sensitive Evaluation Metrics for Federated Learning
by: Li, Yanli, et al.
Published: (2024)
by: Li, Yanli, 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)
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)
Embracing Federated Learning: Enabling Weak Client Participation via Partial Model Training
by: Lee, Sunwoo, et al.
Published: (2024)
by: Lee, Sunwoo, et al.
Published: (2024)
FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale Neural Networks through Federated Learning
by: Chen, Yuanyuan, et al.
Published: (2022)
by: Chen, Yuanyuan, et al.
Published: (2022)
GPT-FL: Generative Pre-trained Model-Assisted Federated Learning
by: Zhang, Tuo, et al.
Published: (2023)
by: Zhang, Tuo, et al.
Published: (2023)
Class-Wise Federated Averaging for Efficient Personalization
by: Lee, Gyuejeong, et al.
Published: (2024)
by: Lee, Gyuejeong, et al.
Published: (2024)
Blockchain-Enabled Federated Learning
by: Rangwala, Murtaza, et al.
Published: (2025)
by: Rangwala, Murtaza, 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)
Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning
by: Fenoglio, Dario, et al.
Published: (2024)
by: Fenoglio, Dario, et al.
Published: (2024)
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)
EcoLearn: Optimizing the Carbon Footprint of Federated Learning
by: Mehboob, Talha, et al.
Published: (2023)
by: Mehboob, Talha, et al.
Published: (2023)
Federated Graph Learning with Graphless Clients
by: Fu, Xingbo, et al.
Published: (2024)
by: Fu, Xingbo, et al.
Published: (2024)
Empowering Data Mesh with Federated Learning
by: Li, Haoyuan, et al.
Published: (2024)
by: Li, Haoyuan, et al.
Published: (2024)
Disentangling data distribution for Federated Learning
by: Zhao, Xinyuan, et al.
Published: (2024)
by: Zhao, Xinyuan, et al.
Published: (2024)
Balancing Similarity and Complementarity for Federated Learning
by: Yan, Kunda, et al.
Published: (2024)
by: Yan, Kunda, et al.
Published: (2024)
Similar Items
-
Algorithms for Collaborative Machine Learning under Statistical Heterogeneity
by: Hahn, Seok-Ju
Published: (2024) -
Towards Seamless Hierarchical Federated Learning under Intermittent Client Participation: A Stagewise Decision-Making Methodology
by: Wu, Minghong, et al.
Published: (2025) -
BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoT
by: Ju, Zehao, et al.
Published: (2024) -
Corrected with the Latest Version: Make Robust Asynchronous Federated Learning Possible
by: Lu, Chaoyi, et al.
Published: (2025) -
Asynchronous Personalized Federated Learning through Global Memorization
by: Wan, Fan, et al.
Published: (2025)