Cover Image

Update Estimation and Scheduling for Over-the-Air Federated Learning with Energy Harvesting Devices

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Bagci, Furkan, Tegin, Busra, Kazemi, Mohammad, Duman, Tolga M.
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Distributed, Parallel, and Cluster Computing
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Over-the-Air Multi-Sensor Inference with Neural Networks Using Memristor-Based Analog Computing
by: Tegin, Busra, et al.
Published: (2025)

Timely Parameter Updating in Over-the-Air Federated Learning
by: Zhu, Jiaqi, et al.
Published: (2025)

Device Scheduling and Assignment in Hierarchical Federated Learning for Internet of Things
by: Zhang, Tinghao, et al.
Published: (2024)

Communication-Efficient Device Scheduling for Federated Learning Using Lyapunov Optimization
by: Perazzone, Jake B., et al.
Published: (2025)

Device Association and Resource Allocation for Hierarchical Split Federated Learning in Space-Air-Ground Integrated Network
by: Zhao, Haitao, et al.
Published: (2026)

DeFRiS: Silo-Cooperative IoT Applications Scheduling via Decentralized Federated Reinforcement Learning
by: Wang, Zhiyu, et al.
Published: (2026)

Scalable and Reliable Over-the-Air Federated Edge Learning
by: Egger, Maximilian, et al.
Published: (2024)

Scheduling for On-Board Federated Learning with Satellite Clusters
by: Razmi, Nasrin, et al.
Published: (2024)

Age-Aware Partial Gradient Update Strategy for Federated Learning Over the Air
by: Du, Ruihao, et al.
Published: (2025)

FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware Scheduler
by: Li, Zilinghan, et al.
Published: (2023)

Learned Digital Codes for Over-the-Air Computation in Federated Edge Learning
by: Tarizzo, Antonio, et al.
Published: (2025)

BLOSSOM: Block-wise Federated Learning Over Shared and Sparse Observed Modalities
by: R, Pranav M, et al.
Published: (2026)

Constrained Over-the-Air Model Updating for Wireless Online Federated Learning with Delayed Information
by: Wang, Juncheng, et al.
Published: (2025)

Boosting Resource-Constrained Federated Learning Systems with Guessed Updates
by: Boukhari, Mohamed Yassine, et al.
Published: (2021)

Prioritizing Modalities: Flexible Importance Scheduling in Federated Multimodal Learning
by: Bian, Jieming, et al.
Published: (2024)

Communication-Efficient Distributed Training for Collaborative Flat Optima Recovery in Deep Learning
by: Dimlioglu, Tolga, et al.
Published: (2025)

Age-of-Gradient Updates for Federated Learning over Random Access Channels
by: Wu, Yu Heng, et al.
Published: (2024)

Resource-Element Energy Difference for Noncoherent Over-the-Air Federated Learning
by: Chen, Hao, et al.
Published: (2026)

MimiC: Combating Client Dropouts in Federated Learning by Mimicking Central Updates
by: Sun, Yuchang, et al.
Published: (2023)

A Robust Federated Learning Framework for Undependable Devices at Scale
by: Wang, Shilong, et al.
Published: (2024)

FedAR: Addressing Client Unavailability in Federated Learning with Local Update Approximation and Rectification
by: Jiang, Chutian, et al.
Published: (2024)

A Joint Approach to Local Updating and Gradient Compression for Efficient Asynchronous Federated Learning
by: Song, Jiajun, et al.
Published: (2024)

Hierarchical Federated Learning with SignSGD: A Highly Communication-Efficient Approach
by: Kazemi, Amirreza, et al.
Published: (2026)

FedTeddi: Temporal Drift and Divergence Aware Scheduling for Timely Federated Edge Learning
by: Bai, Yuxuan, et al.
Published: (2025)

Lumos: Heterogeneity-aware Federated Graph Learning over Decentralized Devices
by: Pan, Qiying, et al.
Published: (2023)

Tackling Intertwined Data and Device Heterogeneities in Federated Learning with Unlimited Staleness
by: Wang, Haoming, et al.
Published: (2023)

Demo: A Practical Testbed for Decentralized Federated Learning on Physical Edge Devices
by: Feng, Chao, et al.
Published: (2025)

Flexible Personalized Split Federated Learning for On-Device Fine-Tuning of Foundation Models
by: Yuan, Tianjun, et al.
Published: (2025)

Efficient Data Distribution Estimation for Accelerated Federated Learning
by: Wang, Yuanli, et al.
Published: (2024)

Fast Decentralized Gradient Tracking for Federated Minimax Optimization with Local Updates
by: Li, Chris Junchi
Published: (2024)

FedNMUT -- Federated Noisy Model Update Tracking Convergence Analysis
by: Chellapandi, Vishnu Pandi, et al.
Published: (2024)

pMixFed: Efficient Personalized Federated Learning through Adaptive Layer-Wise Mixup
by: Saadati, Yasaman, et al.
Published: (2025)

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling
by: Zhao, Xiyu, et al.
Published: (2025)

FedZero: Leveraging Renewable Excess Energy in Federated Learning
by: Wiesner, Philipp, et al.
Published: (2023)

FedEx: Expediting Federated Learning over Heterogeneous Mobile Devices by Overlapping and Participant Selection
by: Geng, Jiaxiang, et al.
Published: (2024)

MultiTASC++: A Continuously Adaptive Scheduler for Edge-Based Multi-Device Cascade Inference
by: Nikolaidis, Sokratis, et al.
Published: (2024)

Energy-Efficient Quantized Federated Learning for Resource-constrained IoT devices
by: Compaoré, Wilfrid Sougrinoma, et al.
Published: (2025)

BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoT
by: Ju, Zehao, et al.
Published: (2024)

Robust Federated Learning with Global Sensitivity Estimation for Financial Risk Management
by: Zhao, Lei, et al.
Published: (2025)

PracMHBench: Re-evaluating Model-Heterogeneous Federated Learning Based on Practical Edge Device Constraints
by: Guo, Yuanchun, et al.
Published: (2025)