Saved in:
| Main Authors: | Wu, Zhiyuan, He, Tianliu, Sun, Sheng, Wang, Yuwei, Liu, Min, Gao, Bo, Jiang, Xuefeng |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.00622 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Agglomerative Federated Learning: Empowering Larger Model Training via End-Edge-Cloud Collaboration
by: Wu, Zhiyuan, et al.
Published: (2023)
by: Wu, Zhiyuan, et al.
Published: (2023)
Knowledge Distillation in Federated Edge Learning: A Survey
by: Wu, Zhiyuan, et al.
Published: (2023)
by: Wu, Zhiyuan, et al.
Published: (2023)
FedICT: Federated Multi-task Distillation for Multi-access Edge Computing
by: Wu, Zhiyuan, et al.
Published: (2023)
by: Wu, Zhiyuan, et al.
Published: (2023)
Privacy-Enhanced Training-as-a-Service for On-Device Intelligence: Concept, Architectural Scheme, and Open Problems
by: Wu, Zhiyuan, et al.
Published: (2024)
by: Wu, Zhiyuan, et al.
Published: (2024)
FedCache 2.0: Federated Edge Learning with Knowledge Caching and Dataset Distillation
by: Pan, Quyang, et al.
Published: (2024)
by: Pan, Quyang, et al.
Published: (2024)
Logits Poisoning Attack in Federated Distillation
by: Tang, Yuhan, et al.
Published: (2024)
by: Tang, Yuhan, et al.
Published: (2024)
Peak-Controlled Logits Poisoning Attack in Federated Distillation
by: Tang, Yuhan, et al.
Published: (2024)
by: Tang, Yuhan, et al.
Published: (2024)
Beyond Model Scale Limits: End-Edge-Cloud Federated Learning with Self-Rectified Knowledge Agglomeration
by: Wu, Zhiyuan, et al.
Published: (2025)
by: Wu, Zhiyuan, 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)
Cross-Class Feature Augmentation for Class Incremental Learning
by: Kim, Taehoon, et al.
Published: (2023)
by: Kim, Taehoon, et al.
Published: (2023)
Hybrid Memory Replay: Blending Real and Distilled Data for Class Incremental Learning
by: Kong, Jiangtao, et al.
Published: (2024)
by: Kong, Jiangtao, et al.
Published: (2024)
Federated Class-Incremental Learning with Hierarchical Generative Prototypes
by: Salami, Riccardo, et al.
Published: (2024)
by: Salami, Riccardo, et al.
Published: (2024)
Class-wise Balancing Data Replay for Federated Class-Incremental Learning
by: Qi, Zhuang, et al.
Published: (2025)
by: Qi, Zhuang, et al.
Published: (2025)
Autoencoder-Based Hybrid Replay for Class-Incremental Learning
by: Nori, Milad Khademi, et al.
Published: (2025)
by: Nori, Milad Khademi, et al.
Published: (2025)
Robust Federated Learning against Noisy Clients via Masked Optimization
by: Jiang, Xuefeng, et al.
Published: (2025)
by: Jiang, Xuefeng, et al.
Published: (2025)
Enhancing Federated Class-Incremental Learning via Spatial-Temporal Statistics Aggregation
by: Guan, Zenghao, et al.
Published: (2025)
by: Guan, Zenghao, et al.
Published: (2025)
On Distilling the Displacement Knowledge for Few-Shot Class-Incremental Learning
by: Fang, Pengfei, et al.
Published: (2024)
by: Fang, Pengfei, et al.
Published: (2024)
Partial Knowledge Distillation for Alleviating the Inherent Inter-Class Discrepancy in Federated Learning
by: Gan, Xiaoyu, et al.
Published: (2024)
by: Gan, Xiaoyu, et al.
Published: (2024)
Dynamic Integration of Task-Specific Adapters for Class Incremental Learning
by: Li, Jiashuo, et al.
Published: (2024)
by: Li, Jiashuo, et al.
Published: (2024)
Reducing Class-wise Confusion for Incremental Learning with Disentangled Manifolds
by: Chen, Huitong, et al.
Published: (2025)
by: Chen, Huitong, et al.
Published: (2025)
PIP: Prototypes-Injected Prompt for Federated Class Incremental Learning
by: Ma'sum, Muhammad Anwar, et al.
Published: (2024)
by: Ma'sum, Muhammad Anwar, et al.
Published: (2024)
Class-Incremental Learning: A Survey
by: Zhou, Da-Wei, et al.
Published: (2023)
by: Zhou, Da-Wei, et al.
Published: (2023)
Confidence Self-Calibration for Multi-Label Class-Incremental Learning
by: Du, Kaile, et al.
Published: (2024)
by: Du, Kaile, et al.
Published: (2024)
Annotation-Free Class-Incremental Learning
by: Kuchibhotla, Hari Chandana, et al.
Published: (2025)
by: Kuchibhotla, Hari Chandana, et al.
Published: (2025)
Class Incremental Learning for Algorithm Selection
by: Nemeth, Mate Botond, et al.
Published: (2025)
by: Nemeth, Mate Botond, et al.
Published: (2025)
Balancing the Causal Effects in Class-Incremental Learning
by: Zheng, Junhao, et al.
Published: (2024)
by: Zheng, Junhao, et al.
Published: (2024)
Few-Shot Class Incremental Learning with Attention-Aware Self-Adaptive Prompt
by: Liu, Chenxi, et al.
Published: (2024)
by: Liu, Chenxi, et al.
Published: (2024)
FedGTEA: Federated Class-Incremental Learning with Gaussian Task Embedding and Alignment
by: Li, Haolin, et al.
Published: (2025)
by: Li, Haolin, et al.
Published: (2025)
Future-Proofing Class-Incremental Learning
by: Jodelet, Quentin, et al.
Published: (2024)
by: Jodelet, Quentin, et al.
Published: (2024)
On the Learning with Augmented Class via Forests
by: Xu, Fan, et al.
Published: (2025)
by: Xu, Fan, et al.
Published: (2025)
Towards Realistic Class-Incremental Learning with Free-Flow Increments
by: Xu, Zhiming, et al.
Published: (2026)
by: Xu, Zhiming, et al.
Published: (2026)
Exemplar-condensed Federated Class-incremental Learning
by: Sun, Rui, et al.
Published: (2024)
by: Sun, Rui, et al.
Published: (2024)
Few-Shot Class-Incremental Learning with Prior Knowledge
by: Jiang, Wenhao, et al.
Published: (2024)
by: Jiang, Wenhao, et al.
Published: (2024)
Federated Class-Incremental Learning: A Hybrid Approach Using Latent Exemplars and Data-Free Techniques to Address Local and Global Forgetting
by: Nori, Milad Khademi, et al.
Published: (2025)
by: Nori, Milad Khademi, et al.
Published: (2025)
Causally Sufficient and Necessary Feature Expansion for Class-Incremental Learning
by: Zhang, Zhen, et al.
Published: (2026)
by: Zhang, Zhen, et al.
Published: (2026)
Closing the Oracle Gap: Increment Vector Transformation for Class Incremental Learning
by: Qiu, Zihuan, et al.
Published: (2025)
by: Qiu, Zihuan, et al.
Published: (2025)
NTK-Guided Few-Shot Class Incremental Learning
by: Liu, Jingren, et al.
Published: (2024)
by: Liu, Jingren, et al.
Published: (2024)
MCIGLE: Multimodal Exemplar-Free Class-Incremental Graph Learning
by: You, Haochen, et al.
Published: (2025)
by: You, Haochen, et al.
Published: (2025)
Text-Enhanced Data-free Approach for Federated Class-Incremental Learning
by: Tran, Minh-Tuan, et al.
Published: (2024)
by: Tran, Minh-Tuan, et al.
Published: (2024)
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)
Similar Items
-
Agglomerative Federated Learning: Empowering Larger Model Training via End-Edge-Cloud Collaboration
by: Wu, Zhiyuan, et al.
Published: (2023) -
Knowledge Distillation in Federated Edge Learning: A Survey
by: Wu, Zhiyuan, et al.
Published: (2023) -
FedICT: Federated Multi-task Distillation for Multi-access Edge Computing
by: Wu, Zhiyuan, et al.
Published: (2023) -
Privacy-Enhanced Training-as-a-Service for On-Device Intelligence: Concept, Architectural Scheme, and Open Problems
by: Wu, Zhiyuan, et al.
Published: (2024) -
FedCache 2.0: Federated Edge Learning with Knowledge Caching and Dataset Distillation
by: Pan, Quyang, et al.
Published: (2024)