Q-LocalAdam: Memory-Efficient Client-Side Adaptive Optimization for Edge Federated Learning
Fuente:
arXiv
Guardado en:
| Autores principales: | Waykole, Vedant, Lone, Haroon R. |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
FedPrism: Adaptive Personalized Federated Learning under Non-IID Data
por: Kumbhakar, Prakash, et al.
Publicado: (2026)
por: Kumbhakar, Prakash, et al.
Publicado: (2026)
Adaptive Federated Learning Defences via Trust-Aware Deep Q-Networks
por: Palit, Vedant
Publicado: (2025)
por: Palit, Vedant
Publicado: (2025)
FedZMG: Efficient Client-Side Optimization in Federated Learning
por: Zantalis, Fotios, et al.
Publicado: (2026)
por: Zantalis, Fotios, et al.
Publicado: (2026)
RiM: Record, Improve and Maintain Physical Well-being using Federated Learning
por: Mishra, Aditya, et al.
Publicado: (2025)
por: Mishra, Aditya, et al.
Publicado: (2025)
Evaluating Federated Learning for Cross-Country Mood Inference from Smartphone Sensing Data
por: Kalpande, Sharmad, et al.
Publicado: (2026)
por: Kalpande, Sharmad, et al.
Publicado: (2026)
CA-AFP: Cluster-Aware Adaptive Federated Pruning
por: Jha, Om Govind, et al.
Publicado: (2026)
por: Jha, Om Govind, et al.
Publicado: (2026)
FedCAda: Adaptive Client-Side Optimization for Accelerated and Stable Federated Learning
por: Zhou, Liuzhi, et al.
Publicado: (2024)
por: Zhou, Liuzhi, et al.
Publicado: (2024)
Federated Distillation on Edge Devices: Efficient Client-Side Filtering for Non-IID Data
por: Mujtaba, Ahmed, et al.
Publicado: (2025)
por: Mujtaba, Ahmed, et al.
Publicado: (2025)
Thermal Vision: Pioneering Non-Invasive Temperature Tracking in Congested Spaces
por: Samal, Arijit, et al.
Publicado: (2024)
por: Samal, Arijit, et al.
Publicado: (2024)
An Adaptive Clustering Scheme for Client Selections in Communication-Efficient Federated Learning
por: Chen, Yan-Ann, et al.
Publicado: (2025)
por: Chen, Yan-Ann, et al.
Publicado: (2025)
Client-Centric Federated Adaptive Optimization
por: Sun, Jianhui, et al.
Publicado: (2025)
por: Sun, Jianhui, et al.
Publicado: (2025)
SpreadFGL: Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation
por: Zhong, Luying, et al.
Publicado: (2024)
por: Zhong, Luying, et al.
Publicado: (2024)
Harnessing Smartwatch Microphone Sensors for Cough Detection and Classification
por: Jaiswal, Pranay, et al.
Publicado: (2024)
por: Jaiswal, Pranay, et al.
Publicado: (2024)
Adaptive Client Selection with Personalization for Communication Efficient Federated Learning
por: de Souza, Allan M., et al.
Publicado: (2024)
por: de Souza, Allan M., et al.
Publicado: (2024)
Client-Side Patching against Backdoor Attacks in Federated Learning
por: Molina-Coronado, Borja
Publicado: (2024)
por: Molina-Coronado, Borja
Publicado: (2024)
Adaptive Client Selection via Q-Learning-based Whittle Index in Wireless Federated Learning
por: Li, Qiyue, et al.
Publicado: (2025)
por: Li, Qiyue, et al.
Publicado: (2025)
FedAWA: Adaptive Optimization of Aggregation Weights in Federated Learning Using Client Vectors
por: Shi, Changlong, et al.
Publicado: (2025)
por: Shi, Changlong, et al.
Publicado: (2025)
HCFSLN: Adaptive Hyperbolic Few-Shot Learning for Multimodal Anxiety Detection
por: Sneh, Aditya, et al.
Publicado: (2025)
por: Sneh, Aditya, et al.
Publicado: (2025)
DECODE: Data-driven Energy Consumption Prediction leveraging Historical Data and Environmental Factors in Buildings
por: Mishra, Aditya, et al.
Publicado: (2023)
por: Mishra, Aditya, et al.
Publicado: (2023)
Online Client Scheduling and Resource Allocation for Efficient Federated Edge Learning
por: Gao, Zhidong, et al.
Publicado: (2024)
por: Gao, Zhidong, et al.
Publicado: (2024)
Enhanced Federated Optimization: Adaptive Unbiased Client Sampling with Reduced Variance
por: Zeng, Dun, et al.
Publicado: (2023)
por: Zeng, Dun, et al.
Publicado: (2023)
Investigating the Generalizability of ECG Noise Detection Across Diverse Data Sources and Noise Types
por: Kalpande, Sharmad, et al.
Publicado: (2025)
por: Kalpande, Sharmad, et al.
Publicado: (2025)
FedHFT: Efficient Federated Finetuning with Heterogeneous Edge Clients
por: Ilhan, Fatih, et al.
Publicado: (2025)
por: Ilhan, Fatih, et al.
Publicado: (2025)
Socially inspired Adaptive Coalition and Client Selection in Federated Learning
por: Licciardi, Alessandro, et al.
Publicado: (2025)
por: Licciardi, Alessandro, et al.
Publicado: (2025)
Communication-Efficient Federated Learning With Data and Client Heterogeneity
por: Zakerinia, Hossein, et al.
Publicado: (2022)
por: Zakerinia, Hossein, et al.
Publicado: (2022)
SIFU: Sequential Informed Federated Unlearning for Efficient and Provable Client Unlearning in Federated Optimization
por: Fraboni, Yann, et al.
Publicado: (2022)
por: Fraboni, Yann, et al.
Publicado: (2022)
Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback
por: Zhao, Boxin, et al.
Publicado: (2021)
por: Zhao, Boxin, et al.
Publicado: (2021)
Adaptive Self-Distillation for Minimizing Client Drift in Heterogeneous Federated Learning
por: Yashwanth, M, et al.
Publicado: (2023)
por: Yashwanth, M, et al.
Publicado: (2023)
Cluster-Based Client Selection for Dependent Multi-Task Federated Learning in Edge Computing
por: Luo, Jieping, et al.
Publicado: (2025)
por: Luo, Jieping, et al.
Publicado: (2025)
Byzantine-Robust Federated Learning: Impact of Client Subsampling and Local Updates
por: Allouah, Youssef, et al.
Publicado: (2024)
por: Allouah, Youssef, et al.
Publicado: (2024)
Why Federated Optimization Fails to Achieve Perfect Fitting? A Theoretical Perspective on Client-Side Optima
por: Lei, Zhongxiang, et al.
Publicado: (2025)
por: Lei, Zhongxiang, et al.
Publicado: (2025)
Communication-Efficient Federated Learning with Accelerated Client Gradient
por: Kim, Geeho, et al.
Publicado: (2022)
por: Kim, Geeho, et al.
Publicado: (2022)
Efficient Client Selection in Federated Learning
por: Marfo, William, et al.
Publicado: (2025)
por: Marfo, William, et al.
Publicado: (2025)
Adaptive Federated Learning with Auto-Tuned Clients
por: Kim, Junhyung Lyle, et al.
Publicado: (2023)
por: Kim, Junhyung Lyle, et al.
Publicado: (2023)
Are Anxiety Detection Models Generalizable? A Cross-Activity and Cross-Population Study Using Wearables
por: Sahu, Nilesh Kumar, et al.
Publicado: (2025)
por: Sahu, Nilesh Kumar, et al.
Publicado: (2025)
CLIP: Client-Side Invariant Pruning for Mitigating Stragglers in Secure Federated Learning
por: DiMaggio, Anthony, et al.
Publicado: (2025)
por: DiMaggio, Anthony, et al.
Publicado: (2025)
Fairness-Aware Few-Shot Learning for Audio-Visual Stress Detection
por: Shelke, Anushka Sanjay, et al.
Publicado: (2025)
por: Shelke, Anushka Sanjay, et al.
Publicado: (2025)
FilFL: Client Filtering for Optimized Client Participation in Federated Learning
por: Fourati, Fares, et al.
Publicado: (2023)
por: Fourati, Fares, et al.
Publicado: (2023)
Heterogeneity-Aware Client Sampling for Optimal and Efficient Federated Learning
por: Weng, Shudi, et al.
Publicado: (2025)
por: Weng, Shudi, et al.
Publicado: (2025)
FedSparQ: Adaptive Sparse Quantization with Error Feedback for Robust & Efficient Federated Learning
por: Medjadji, Chaimaa, et al.
Publicado: (2025)
por: Medjadji, Chaimaa, et al.
Publicado: (2025)
Ejemplares similares
-
FedPrism: Adaptive Personalized Federated Learning under Non-IID Data
por: Kumbhakar, Prakash, et al.
Publicado: (2026) -
Adaptive Federated Learning Defences via Trust-Aware Deep Q-Networks
por: Palit, Vedant
Publicado: (2025) -
FedZMG: Efficient Client-Side Optimization in Federated Learning
por: Zantalis, Fotios, et al.
Publicado: (2026) -
RiM: Record, Improve and Maintain Physical Well-being using Federated Learning
por: Mishra, Aditya, et al.
Publicado: (2025) -
Evaluating Federated Learning for Cross-Country Mood Inference from Smartphone Sensing Data
por: Kalpande, Sharmad, et al.
Publicado: (2026)