Towards Client Driven Federated Learning
Fuente:
arXiv
Saved in:
| Main Authors: | Li, Songze, Zhu, Chenqing |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Tackling Resource-Constrained and Data-Heterogeneity in Federated Learning with Double-Weight Sparse Pack
by: Yang, Qiantao, et al.
Published: (2026)
by: Yang, Qiantao, et al.
Published: (2026)
Federated Graph Learning with Graphless Clients
by: Fu, Xingbo, et al.
Published: (2024)
by: Fu, Xingbo, 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)
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)
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)
Ranking-based Client Selection with Imitation Learning for Efficient Federated Learning
by: Tian, Chunlin, et al.
Published: (2024)
by: Tian, Chunlin, et al.
Published: (2024)
Debiasing Federated Learning with Correlated Client Participation
by: Sun, Zhenyu, et al.
Published: (2024)
by: Sun, Zhenyu, et al.
Published: (2024)
Federated Learning in the Presence of Adversarial Client Unavailability
by: Su, Lili, et al.
Published: (2023)
by: Su, Lili, et al.
Published: (2023)
FedEve: On Bridging the Client Drift and Period Drift for Cross-device Federated Learning
by: Shen, Tao, et al.
Published: (2025)
by: Shen, Tao, et al.
Published: (2025)
Achieving Linear Speedup in Asynchronous Federated Learning with Heterogeneous Clients
by: Wang, Xiaolu, et al.
Published: (2024)
by: Wang, Xiaolu, et al.
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)
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)
Local Gradient Regulation Stabilizes Federated Learning under Client Heterogeneity
by: Luo, Ping, et al.
Published: (2026)
by: Luo, Ping, et al.
Published: (2026)
Toward Malicious Clients Detection in Federated Learning
by: Dou, Zhihao, et al.
Published: (2025)
by: Dou, Zhihao, et al.
Published: (2025)
Exact and Linear Convergence for Federated Learning under Arbitrary Client Participation is Attainable
by: Ying, Bicheng, et al.
Published: (2025)
by: Ying, Bicheng, et al.
Published: (2025)
Emulating Full Participation: An Effective and Fair Client Selection Strategy for Federated Learning
by: Li, Qingming, et al.
Published: (2024)
by: Li, Qingming, et al.
Published: (2024)
Maverick-Aware Shapley Valuation for Client Selection in Federated Learning
by: Yang, Mengwei, et al.
Published: (2024)
by: Yang, Mengwei, et al.
Published: (2024)
Harnessing Increased Client Participation with Cohort-Parallel Federated Learning
by: Dhasade, Akash, et al.
Published: (2024)
by: Dhasade, Akash, et al.
Published: (2024)
Adaptive Client Selection with Personalization for Communication Efficient Federated Learning
by: de Souza, Allan M., et al.
Published: (2024)
by: de Souza, Allan M., 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)
Greedy Shapley Client Selection for Communication-Efficient Federated Learning
by: Singhal, Pranava, et al.
Published: (2023)
by: Singhal, Pranava, et al.
Published: (2023)
Adaptive Client Selection via Q-Learning-based Whittle Index in Wireless Federated Learning
by: Li, Qiyue, et al.
Published: (2025)
by: Li, Qiyue, et al.
Published: (2025)
Communication-Efficient Multimodal Federated Learning: Joint Modality and Client Selection
by: Yuan, Liangqi, et al.
Published: (2024)
by: Yuan, Liangqi, et al.
Published: (2024)
Understanding Server-Assisted Federated Learning in the Presence of Incomplete Client Participation
by: Yang, Haibo, et al.
Published: (2024)
by: Yang, Haibo, et al.
Published: (2024)
Flexible Clustered Federated Learning for Client-Level Data Distribution Shift
by: Duan, Moming, et al.
Published: (2021)
by: Duan, Moming, et al.
Published: (2021)
S-VOTE: Similarity-based Voting for Client Selection in Decentralized Federated Learning
by: Sánchez, Pedro Miguel Sánchez, et al.
Published: (2025)
by: Sánchez, Pedro Miguel Sánchez, et al.
Published: (2025)
FedCAda: Adaptive Client-Side Optimization for Accelerated and Stable Federated Learning
by: Zhou, Liuzhi, et al.
Published: (2024)
by: Zhou, Liuzhi, et al.
Published: (2024)
MimiC: Combating Client Dropouts in Federated Learning by Mimicking Central Updates
by: Sun, Yuchang, et al.
Published: (2023)
by: Sun, Yuchang, et al.
Published: (2023)
Robust Federated Learning in Unreliable Wireless Networks: A Client Selection Approach
by: Wang, Yanmeng, et al.
Published: (2025)
by: Wang, Yanmeng, et al.
Published: (2025)
Overcoming Challenges of Partial Client Participation in Federated Learning : A Comprehensive Review
by: Sen, Mrinmay, et al.
Published: (2025)
by: Sen, Mrinmay, et al.
Published: (2025)
Knowledge-Driven Federated Graph Learning on Model Heterogeneity
by: Wu, Zhengyu, et al.
Published: (2025)
by: Wu, Zhengyu, et al.
Published: (2025)
Towards Federated RLHF with Aggregated Client Preference for LLMs
by: Wu, Feijie, et al.
Published: (2024)
by: Wu, Feijie, et al.
Published: (2024)
CycleSL: Server-Client Cyclical Update Driven Scalable Split Learning
by: Wang, Mengdi, et al.
Published: (2025)
by: Wang, Mengdi, et al.
Published: (2025)
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)
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)
GPFL: A Gradient Projection-Based Client Selection Framework for Efficient Federated Learning
by: Na, Shijie, et al.
Published: (2024)
by: Na, Shijie, et al.
Published: (2024)
FedAR: Addressing Client Unavailability in Federated Learning with Local Update Approximation and Rectification
by: Jiang, Chutian, et al.
Published: (2024)
by: Jiang, Chutian, et al.
Published: (2024)
Reducing Communication Overhead in Federated Learning for Network Anomaly Detection with Adaptive Client Selection
by: Marfo, William, et al.
Published: (2025)
by: Marfo, William, et al.
Published: (2025)
FedGCS: A Generative Framework for Efficient Client Selection in Federated Learning via Gradient-based Optimization
by: Ning, Zhiyuan, et al.
Published: (2024)
by: Ning, Zhiyuan, et al.
Published: (2024)
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)
Similar Items
-
Tackling Resource-Constrained and Data-Heterogeneity in Federated Learning with Double-Weight Sparse Pack
by: Yang, Qiantao, et al.
Published: (2026) -
Federated Graph Learning with Graphless Clients
by: Fu, Xingbo, et al.
Published: (2024) -
Beyond the Federation: Topology-aware Federated Learning for Generalization to Unseen Clients
by: Ma, Mengmeng, et al.
Published: (2024) -
The Diversity Bonus: Learning from Dissimilar Distributed Clients in Personalized Federated Learning
by: Wu, Xinghao, 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)