Greedy Shapley Client Selection for Communication-Efficient Federated Learning
Fuente:
arXiv
Saved in:
| Main Authors: | Singhal, Pranava, Pandey, Shashi Raj, Popovski, Petar |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Maverick-Aware Shapley Valuation for Client Selection in Federated Learning
by: Yang, Mengwei, et al.
Published: (2024)
by: Yang, Mengwei, 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)
Scheduling for On-Board Federated Learning with Satellite Clusters
by: Razmi, Nasrin, et al.
Published: (2024)
by: Razmi, Nasrin, et al.
Published: (2024)
Communication-Efficient Multimodal Federated Learning: Joint Modality and Client Selection
by: Yuan, Liangqi, et al.
Published: (2024)
by: Yuan, Liangqi, 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)
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)
Efficient Client Selection in Federated Learning
by: Marfo, William, et al.
Published: (2025)
by: Marfo, William, et al.
Published: (2025)
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)
Energy-Aware Federated Learning in Satellite Constellations
by: Razmi, Nasrin, et al.
Published: (2024)
by: Razmi, Nasrin, et al.
Published: (2024)
Communication-Efficient Federated AUC Maximization with Cyclic Client Participation
by: Vangapally, Umesh, et al.
Published: (2026)
by: Vangapally, Umesh, et al.
Published: (2026)
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)
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)
Sparse Incremental Aggregation in Multi-Hop Federated Learning
by: Mukherjee, Sourav, et al.
Published: (2024)
by: Mukherjee, Sourav, et al.
Published: (2024)
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)
Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysis
by: Crawshaw, Michael, et al.
Published: (2024)
by: Crawshaw, Michael, et al.
Published: (2024)
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)
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)
Federated Graph Learning with Graphless Clients
by: Fu, Xingbo, et al.
Published: (2024)
by: Fu, Xingbo, et al.
Published: (2024)
Towards Client Driven Federated Learning
by: Li, Songze, et al.
Published: (2024)
by: Li, Songze, et al.
Published: (2024)
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)
Federated Learning in the Presence of Adversarial Client Unavailability
by: Su, Lili, et al.
Published: (2023)
by: Su, Lili, et al.
Published: (2023)
Debiasing Federated Learning with Correlated Client Participation
by: Sun, Zhenyu, et al.
Published: (2024)
by: Sun, Zhenyu, et al.
Published: (2024)
FedHFT: Efficient Federated Finetuning with Heterogeneous Edge Clients
by: Ilhan, Fatih, et al.
Published: (2025)
by: Ilhan, Fatih, 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)
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)
OCD-FL: A Novel Communication-Efficient Peer Selection-based Decentralized Federated Learning
by: Masmoudi, Nizar, et al.
Published: (2024)
by: Masmoudi, Nizar, et al.
Published: (2024)
Achieving Linear Speedup in Asynchronous Federated Learning with Heterogeneous Clients
by: Wang, Xiaolu, et al.
Published: (2024)
by: Wang, Xiaolu, 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)
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)
Optimizing Federated Learning by Entropy-Based Client Selection
by: Lutz, Andreas, et al.
Published: (2024)
by: Lutz, Andreas, 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)
Flexible Clustered Federated Learning for Client-Level Data Distribution Shift
by: Duan, Moming, et al.
Published: (2021)
by: Duan, Moming, et al.
Published: (2021)
Local Gradient Regulation Stabilizes Federated Learning under Client Heterogeneity
by: Luo, Ping, et al.
Published: (2026)
by: Luo, Ping, et al.
Published: (2026)
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)
Federated Distillation on Edge Devices: Efficient Client-Side Filtering for Non-IID Data
by: Mujtaba, Ahmed, et al.
Published: (2025)
by: Mujtaba, Ahmed, et al.
Published: (2025)
Masked Random Noise for Communication Efficient Federated Learning
by: Li, Shiwei, et al.
Published: (2024)
by: Li, Shiwei, et al.
Published: (2024)
Random Client Selection on Contrastive Federated Learning for Tabular Data
by: Ginanjar, Achmad, et al.
Published: (2025)
by: Ginanjar, Achmad, et al.
Published: (2025)
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)
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)
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)
Similar Items
-
Maverick-Aware Shapley Valuation for Client Selection in Federated Learning
by: Yang, Mengwei, et al.
Published: (2024) -
Adaptive Client Selection with Personalization for Communication Efficient Federated Learning
by: de Souza, Allan M., et al.
Published: (2024) -
Scheduling for On-Board Federated Learning with Satellite Clusters
by: Razmi, Nasrin, et al.
Published: (2024) -
Communication-Efficient Multimodal Federated Learning: Joint Modality and Client Selection
by: Yuan, Liangqi, et al.
Published: (2024) -
Heterogeneity-Aware Client Selection Methodology For Efficient Federated Learning
by: Balivada, Nihal, et al.
Published: (2026)