Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimization
Fuente:
arXiv
Saved in:
| Main Authors: | Li, Zhe, Ying, Bicheng, Liu, Zidong, Dong, Chaosheng, Yang, Haibo |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Converge Faster, Talk Less: Hessian-Informed Federated Zeroth-Order Optimization
by: Li, Zhe, et al.
Published: (2025)
by: Li, Zhe, 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)
Federated Multi-Objective Learning
by: Yang, Haibo, et al.
Published: (2023)
by: Yang, Haibo, et al.
Published: (2023)
Communication-Efficient and Differentially Private Vertical Federated Learning with Zeroth-Order Optimization
by: Zhang, Jianing, et al.
Published: (2025)
by: Zhang, Jianing, et al.
Published: (2025)
Hybrid Decentralized Optimization: Leveraging Both First- and Zeroth-Order Optimizers for Faster Convergence
by: Ansaripour, Matin, et al.
Published: (2022)
by: Ansaripour, Matin, et al.
Published: (2022)
When Foresight Pruning Meets Zeroth-Order Optimization: Efficient Federated Learning for Low-Memory Devices
by: Zhang, Pengyu, et al.
Published: (2024)
by: Zhang, Pengyu, et al.
Published: (2024)
Communication and Energy Efficient Federated Learning using Zero-Order Optimization Technique
by: Mhanna, Elissa, et al.
Published: (2024)
by: Mhanna, Elissa, et al.
Published: (2024)
Towards Communication-efficient Federated Learning via Sparse and Aligned Adaptive Optimization
by: Deng, Xiumei, et al.
Published: (2024)
by: Deng, Xiumei, 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)
FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scaler
by: Peng, Hongyi, et al.
Published: (2024)
by: Peng, Hongyi, 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)
Accelerating Wireless Distributed Learning via Hybrid Split and Federated Learning Optimization
by: Guo, Kun, et al.
Published: (2025)
by: Guo, Kun, et al.
Published: (2025)
Communication-Efficient Federated Group Distributionally Robust Optimization
by: Guo, Zhishuai, et al.
Published: (2024)
by: Guo, Zhishuai, et al.
Published: (2024)
Towards Straggler-Resilient Split Federated Learning: An Unbalanced Update Approach
by: Liang, Dandan, et al.
Published: (2025)
by: Liang, Dandan, et al.
Published: (2025)
FedBAT: Communication-Efficient Federated Learning via Learnable Binarization
by: Li, Shiwei, et al.
Published: (2024)
by: Li, Shiwei, et al.
Published: (2024)
FedBiF: Communication-Efficient Federated Learning via Bits Freezing
by: Li, Shiwei, et al.
Published: (2025)
by: Li, Shiwei, 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)
Communication-Efficient Device Scheduling for Federated Learning Using Lyapunov Optimization
by: Perazzone, Jake B., et al.
Published: (2025)
by: Perazzone, Jake B., et al.
Published: (2025)
Byzantine-Resilient Zero-Order Optimization for Communication-Efficient Heterogeneous Federated Learning
by: Egger, Maximilian, et al.
Published: (2025)
by: Egger, Maximilian, et al.
Published: (2025)
Review of Mathematical Optimization in Federated Learning
by: Yang, Shusen, et al.
Published: (2024)
by: Yang, Shusen, et al.
Published: (2024)
Computation and Communication Efficient Lightweighting Vertical Federated Learning for Smart Building IoT
by: Wang, Heqiang, et al.
Published: (2024)
by: Wang, Heqiang, et al.
Published: (2024)
Federated Communication-Efficient Multi-Objective Optimization
by: Askin, Baris, et al.
Published: (2024)
by: Askin, Baris, et al.
Published: (2024)
Rehearsal-Free Continual Federated Learning with Synergistic Synaptic Intelligence
by: Li, Yichen, et al.
Published: (2024)
by: Li, Yichen, 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)
Communication-Efficient Distributed Deep Learning via Federated Dynamic Averaging
by: Theologitis, Michail, et al.
Published: (2024)
by: Theologitis, Michail, et al.
Published: (2024)
Robust and Communication-Efficient Federated Domain Adaptation via Random Features
by: Feng, Zhanbo, et al.
Published: (2023)
by: Feng, Zhanbo, et al.
Published: (2023)
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)
The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning
by: Li, Shiwei, et al.
Published: (2025)
by: Li, Shiwei, et al.
Published: (2025)
Robust Model Aggregation for Heterogeneous Federated Learning: Analysis and Optimizations
by: Shao, Yumeng, et al.
Published: (2024)
by: Shao, Yumeng, et al.
Published: (2024)
FEDBUD: Joint Incentive and Privacy Optimization for Resource-Constrained Federated Learning
by: Liu, Tao, et al.
Published: (2026)
by: Liu, Tao, et al.
Published: (2026)
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)
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)
EcoLearn: Optimizing the Carbon Footprint of Federated Learning
by: Mehboob, Talha, et al.
Published: (2023)
by: Mehboob, Talha, et al.
Published: (2023)
History Rhymes: Accelerating LLM Reinforcement Learning with RhymeRL
by: He, Jingkai, et al.
Published: (2025)
by: He, Jingkai, et al.
Published: (2025)
FUPareto: Bridging the Forgetting-Utility Gap in Federated Unlearning via Pareto Augmented Optimization
by: Wang, Zeyan, et al.
Published: (2026)
by: Wang, Zeyan, et al.
Published: (2026)
Federated Graph Learning with Graphless Clients
by: Fu, Xingbo, et al.
Published: (2024)
by: Fu, Xingbo, et al.
Published: (2024)
Collaborative Batch Size Optimization for Federated Learning
by: Geimer, Arno, et al.
Published: (2025)
by: Geimer, Arno, et al.
Published: (2025)
Personalized Federated Learning for Generative AI-Assisted Semantic Communications
by: Peng, Yubo, et al.
Published: (2024)
by: Peng, Yubo, et al.
Published: (2024)
DFDG: Data-Free Dual-Generator Adversarial Distillation for One-Shot Federated Learning
by: Luo, Kangyang, et al.
Published: (2024)
by: Luo, Kangyang, et al.
Published: (2024)
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)
Similar Items
-
Converge Faster, Talk Less: Hessian-Informed Federated Zeroth-Order Optimization
by: Li, Zhe, et al.
Published: (2025) -
Exact and Linear Convergence for Federated Learning under Arbitrary Client Participation is Attainable
by: Ying, Bicheng, et al.
Published: (2025) -
Federated Multi-Objective Learning
by: Yang, Haibo, et al.
Published: (2023) -
Communication-Efficient and Differentially Private Vertical Federated Learning with Zeroth-Order Optimization
by: Zhang, Jianing, et al.
Published: (2025) -
Hybrid Decentralized Optimization: Leveraging Both First- and Zeroth-Order Optimizers for Faster Convergence
by: Ansaripour, Matin, et al.
Published: (2022)