Copertina

FeDMRA: Federated Incremental Learning with Dynamic Memory Replay Allocation

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Wang, Tiantian, Xiang, Xiang, Du, Simon S.
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition Distributed, Parallel, and Cluster Computing
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Data-Free Federated Class Incremental Learning with Diffusion-Based Generative Memory
di: Wang, Naibo, et al.
Pubblicazione: (2024)

FedDUAL: A Dual-Strategy with Adaptive Loss and Dynamic Aggregation for Mitigating Data Heterogeneity in Federated Learning
di: Sahoo, Pranab, et al.
Pubblicazione: (2024)

Flashback: Understanding and Mitigating Forgetting in Federated Learning
di: Aljahdali, Mohammed, et al.
Pubblicazione: (2024)

Federated Learning Client Pruning for Noisy Labels
di: Morafah, Mahdi, et al.
Pubblicazione: (2024)

Federated Impression for Learning with Distributed Heterogeneous Data
di: Arya, Atrin, et al.
Pubblicazione: (2024)

FedWon: Triumphing Multi-domain Federated Learning Without Normalization
di: Zhuang, Weiming, et al.
Pubblicazione: (2023)

Caching Techniques for Reducing the Communication Cost of Federated Learning in IoT Environments
di: Alhonainy, Ahmad, et al.
Pubblicazione: (2025)

Training Together, Diagnosing Better: Federated Learning for Collagen VI-Related Dystrophies
di: Brull, Astrid, et al.
Pubblicazione: (2025)

FedDr+: Stabilizing Dot-regression with Global Feature Distillation for Federated Learning
di: Kim, Seongyoon, et al.
Pubblicazione: (2024)

FedMeNF: Privacy-Preserving Federated Meta-Learning for Neural Fields
di: Yun, Junhyeog, et al.
Pubblicazione: (2025)

FAST: Federated Active Learning with Foundation Models for Communication-efficient Sampling and Training
di: Li, Haoyuan, et al.
Pubblicazione: (2025)

Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy
di: Ukaye, Asim, et al.
Pubblicazione: (2026)

Towards Diverse Device Heterogeneous Federated Learning via Task Arithmetic Knowledge Integration
di: Morafah, Mahdi, et al.
Pubblicazione: (2024)

FedMRL: Data Heterogeneity Aware Federated Multi-agent Deep Reinforcement Learning for Medical Imaging
di: Sahoo, Pranab, et al.
Pubblicazione: (2024)

Towards Efficient Replay in Federated Incremental Learning
di: Li, Yichen, et al.
Pubblicazione: (2024)

Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data
di: Chunduru, Abhijit, et al.
Pubblicazione: (2025)

FedSAC: Dynamic Submodel Allocation for Collaborative Fairness in Federated Learning
di: Wang, Zihui, et al.
Pubblicazione: (2024)

Task-Agnostic Federated Learning
di: Yao, Zhengtao, et al.
Pubblicazione: (2024)

Beyond Corner Patches: Semantics-Aware Backdoor Attack in Federated Learning
di: Herath, Kavindu, et al.
Pubblicazione: (2026)

TraceFL: Interpretability-Driven Debugging in Federated Learning via Neuron Provenance
di: Gill, Waris, et al.
Pubblicazione: (2023)

Spatial-Temporal Federated Learning for Lifelong Person Re-identification on Distributed Edges
di: Zhang, Lei, et al.
Pubblicazione: (2022)

FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels
di: Gholami, Sina, et al.
Pubblicazione: (2026)

Federated Learning for Diffusion Models
di: Peng, Zihao, et al.
Pubblicazione: (2025)

LV-XAttn: Distributed Cross-Attention for Long Visual Inputs in Multimodal Large Language Models
di: Chang, Tzu-Tao, et al.
Pubblicazione: (2025)

Beyond Inference: Performance Analysis of DNN Server Overheads for Computer Vision
di: AbouElhamayed, Ahmed F., et al.
Pubblicazione: (2024)

AccidentGPT: Large Multi-Modal Foundation Model for Traffic Accident Analysis
di: Wu, Kebin, et al.
Pubblicazione: (2024)

Distance-Restricted Explanations: Theoretical Underpinnings & Efficient Implementation
di: Izza, Yacine, et al.
Pubblicazione: (2024)

CommFuse: Hiding Tail Latency via Communication Decomposition and Fusion for Distributed LLM Training
di: Karim, Rezaul, et al.
Pubblicazione: (2026)

Efficiently Serving Large Multimodal Models Using EPD Disaggregation
di: Singh, Gursimran, et al.
Pubblicazione: (2024)

Distributed Hybrid Parallelism for Large Language Models: Comparative Study and System Design Guide
di: Amer, Hossam, et al.
Pubblicazione: (2026)

Local Performance vs. Out-of-Distribution Generalization: An Empirical Analysis of Personalized Federated Learning in Heterogeneous Data Environments
di: Hussaini, Mortesa, et al.
Pubblicazione: (2025)

Recurrent Early Exits for Federated Learning with Heterogeneous Clients
di: Lee, Royson, et al.
Pubblicazione: (2024)

Energy-Efficient Federated Learning via Adaptive Encoder Freezing for MRI-to-CT Conversion: A Green AI-Guided Research
di: Raggio, Ciro Benito, et al.
Pubblicazione: (2025)

On Distributed Larger-Than-Memory Subset Selection With Pairwise Submodular Functions
di: Böther, Maximilian, et al.
Pubblicazione: (2024)

Online Client Scheduling and Resource Allocation for Efficient Federated Edge Learning
di: Gao, Zhidong, et al.
Pubblicazione: (2024)

FedRepOpt: Gradient Re-parametrized Optimizers in Federated Learning
di: Lau, Kin Wai, et al.
Pubblicazione: (2024)

Cross-Silo Federated Learning Across Divergent Domains with Iterative Parameter Alignment
di: Gorbett, Matt, et al.
Pubblicazione: (2023)

FedDyMem: Efficient Federated Learning with Dynamic Memory and Memory-Reduce for Unsupervised Image Anomaly Detection
di: Chen, Silin, et al.
Pubblicazione: (2025)

Heterogeneity-Aware Resource Allocation and Topology Design for Hierarchical Federated Edge Learning
di: Gao, Zhidong, et al.
Pubblicazione: (2024)

FedPylot: Navigating Federated Learning for Real-Time Object Detection in Internet of Vehicles
di: Quéméneur, Cyprien, et al.
Pubblicazione: (2024)