Saved in:
| Main Authors: | Kim, Geeho, Kim, Jinkyu, Han, Bohyung |
|---|---|
| Format: | Preprint |
| Published: |
2022
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2201.03172 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Relaxed Contrastive Learning for Federated Learning
by: Seo, Seonguk, et al.
Published: (2024)
by: Seo, Seonguk, et al.
Published: (2024)
Merge and Bound: Direct Manipulations on Weights for Class Incremental Learning
by: Kim, Taehoon, et al.
Published: (2025)
by: Kim, Taehoon, et al.
Published: (2025)
C2A: Client-Customized Adaptation for Parameter-Efficient Federated Learning
by: Kim, Yeachan, et al.
Published: (2024)
by: Kim, Yeachan, et al.
Published: (2024)
GC-Fed: Gradient Centralized Federated Learning with Partial Client Participation
by: Seo, Jungwon, et al.
Published: (2025)
by: Seo, Jungwon, et al.
Published: (2025)
FedBCD:Communication-Efficient Accelerated Block Coordinate Gradient Descent for Federated Learning
by: Liu, Junkang, et al.
Published: (2026)
by: Liu, Junkang, et al.
Published: (2026)
Score-Repellent Monte Carlo: Toward Efficient Non-Markovian Sampler with Constant Memory in General State Spaces
by: Hu, Jie, et al.
Published: (2026)
by: Hu, Jie, et al.
Published: (2026)
FedZMG: Efficient Client-Side Optimization in Federated Learning
by: Zantalis, Fotios, et al.
Published: (2026)
by: Zantalis, Fotios, et al.
Published: (2026)
Heterogeneity-Aware Client Sampling for Optimal and Efficient Federated Learning
by: Weng, Shudi, et al.
Published: (2025)
by: Weng, Shudi, et al.
Published: (2025)
AGDC: Autoregressive Generation of Variable-Length Sequences with Joint Discrete and Continuous Spaces
by: Shin, Yeonsang, et al.
Published: (2026)
by: Shin, Yeonsang, et al.
Published: (2026)
Efficient Client Selection in Federated Learning
by: Marfo, William, et al.
Published: (2025)
by: Marfo, William, et al.
Published: (2025)
Enhanced Diffusion Sampling via Extrapolation with Multiple ODE Solutions
by: Choi, Jinyoung, et al.
Published: (2025)
by: Choi, Jinyoung, et al.
Published: (2025)
Noise-aware Client Selection for carbon-efficient Federated Learning via Gradient Norm Thresholding
by: Wilhelm, Patrick, et al.
Published: (2026)
by: Wilhelm, Patrick, et al.
Published: (2026)
FedRG: Unleashing the Representation Geometry for Federated Learning with Noisy Clients
by: Wen, Tian, et al.
Published: (2026)
by: Wen, Tian, et al.
Published: (2026)
LSHFed: Robust and Communication-Efficient Federated Learning with Locally-Sensitive Hashing Gradient Mapping
by: Cheng, Guanjie, et al.
Published: (2025)
by: Cheng, Guanjie, et al.
Published: (2025)
FedConv: A Learning-on-Model Paradigm for Heterogeneous Federated Clients
by: Shen, Leming, et al.
Published: (2025)
by: Shen, Leming, et al.
Published: (2025)
LION-DG: Layer-Informed Initialization with Deep Gradient Protocols for Accelerated Neural Network Training
by: Kim, Hyunjun
Published: (2026)
by: Kim, Hyunjun
Published: (2026)
Observation-Guided Diffusion Probabilistic Models
by: Kang, Junoh, et al.
Published: (2023)
by: Kang, Junoh, et al.
Published: (2023)
Federated Learning with Sample-level Client Drift Mitigation
by: Xu, Haoran, et al.
Published: (2025)
by: Xu, Haoran, et al.
Published: (2025)
Unlearning Clients, Features and Samples in Vertical Federated Learning
by: Varshney, Ayush K., et al.
Published: (2025)
by: Varshney, Ayush K., et al.
Published: (2025)
Grokfast: Accelerated Grokking by Amplifying Slow Gradients
by: Lee, Jaerin, et al.
Published: (2024)
by: Lee, Jaerin, et al.
Published: (2024)
FedConPE: Efficient Federated Conversational Bandits with Heterogeneous Clients
by: Li, Zhuohua, et al.
Published: (2024)
by: Li, Zhuohua, et al.
Published: (2024)
An Interpretable Client Decision Tree Aggregation process for Federated Learning
by: Argente-Garrido, Alberto, et al.
Published: (2024)
by: Argente-Garrido, Alberto, et al.
Published: (2024)
Local Data Quantity-Aware Weighted Averaging for Federated Learning with Dishonest Clients
by: Wu, Leming, et al.
Published: (2025)
by: Wu, Leming, et al.
Published: (2025)
Detecting Atypical Clients in Federated Learning via Representation-Level Divergence
by: Pérez-Corral, Cristian, et al.
Published: (2026)
by: Pérez-Corral, Cristian, et al.
Published: (2026)
Enhancing Federated Learning Through Secure Cluster-Weighted Client Aggregation
by: Ranaweera, Kanishka, et al.
Published: (2025)
by: Ranaweera, Kanishka, et al.
Published: (2025)
Tackling Noisy Clients in Federated Learning with End-to-end Label Correction
by: Jiang, Xuefeng, et al.
Published: (2024)
by: Jiang, Xuefeng, et al.
Published: (2024)
FedCCRL: Federated Domain Generalization with Cross-Client Representation Learning
by: Wang, Xinpeng, et al.
Published: (2024)
by: Wang, Xinpeng, et al.
Published: (2024)
Online Client Scheduling and Resource Allocation for Efficient Federated Edge Learning
by: Gao, Zhidong, et al.
Published: (2024)
by: Gao, Zhidong, et al.
Published: (2024)
Federated Linear Contextual Bandits with Heterogeneous Clients
by: Blaser, Ethan, et al.
Published: (2024)
by: Blaser, Ethan, et al.
Published: (2024)
Acceleration of Grokking in Learning Arithmetic Operations via Kolmogorov-Arnold Representation
by: Park, Yeachan, et al.
Published: (2024)
by: Park, Yeachan, et al.
Published: (2024)
Gradients as an Action: Towards Communication-Efficient Federated Recommender Systems via Adaptive Action Sharing
by: Lu, Zhufeng, et al.
Published: (2025)
by: Lu, Zhufeng, et al.
Published: (2025)
FilFL: Client Filtering for Optimized Client Participation in Federated Learning
by: Fourati, Fares, et al.
Published: (2023)
by: Fourati, Fares, et al.
Published: (2023)
Tackling Selfish Clients in Federated Learning
by: Augello, Andrea, et al.
Published: (2024)
by: Augello, Andrea, et al.
Published: (2024)
History-Aware and Dynamic Client Contribution in Federated Learning
by: Ghosh, Bishwamittra, et al.
Published: (2024)
by: Ghosh, Bishwamittra, et al.
Published: (2024)
FedImpro: Measuring and Improving Client Update in Federated Learning
by: Tang, Zhenheng, et al.
Published: (2024)
by: Tang, Zhenheng, et al.
Published: (2024)
Improved Generalization Bounds for Communication Efficient Federated Learning
by: Gholami, Peyman, et al.
Published: (2024)
by: Gholami, Peyman, et al.
Published: (2024)
Cumulative Utility Parity for Fair Federated Learning under Intermittent Client Participation
by: Behfar, Stefan, et al.
Published: (2026)
by: Behfar, Stefan, et al.
Published: (2026)
Addressing Data Quality Decompensation in Federated Learning via Dynamic Client Selection
by: Fei, Qinjun, et al.
Published: (2025)
by: Fei, Qinjun, et al.
Published: (2025)
NeFL: Nested Model Scaling for Federated Learning with System Heterogeneous Clients
by: Kang, Honggu, et al.
Published: (2023)
by: Kang, Honggu, et al.
Published: (2023)
Revisiting Softmax Masking: Stop Gradient for Enhancing Stability in Replay-based Continual Learning
by: Kim, Hoyong, et al.
Published: (2023)
by: Kim, Hoyong, et al.
Published: (2023)
Similar Items
-
Relaxed Contrastive Learning for Federated Learning
by: Seo, Seonguk, et al.
Published: (2024) -
Merge and Bound: Direct Manipulations on Weights for Class Incremental Learning
by: Kim, Taehoon, et al.
Published: (2025) -
C2A: Client-Customized Adaptation for Parameter-Efficient Federated Learning
by: Kim, Yeachan, et al.
Published: (2024) -
GC-Fed: Gradient Centralized Federated Learning with Partial Client Participation
by: Seo, Jungwon, et al.
Published: (2025) -
FedBCD:Communication-Efficient Accelerated Block Coordinate Gradient Descent for Federated Learning
by: Liu, Junkang, et al.
Published: (2026)