Federated Learning

Federated Learning is a machine learning approach that allows multiple devices or entities to collaboratively train a shared model without exchanging their data with each other. Instead of sending data to a central server for training, the model is trained locally on each device, and only the model updates are sent to the central server, where they are aggregated to improve the shared model.

This approach allows for privacy-preserving machine learning, as each device keeps its data locally and only shares the information needed to improve the model.

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 6501–6525 of 6771 papers

Title	Date	Tasks	Status
Energizing Federated Learning via Filter-Aware Attention	Nov 18, 2023	Federated Learning	—Unverified
Energy and Spectrum Efficient Federated Learning via High-Precision Over-the-Air Computation	Aug 15, 2022	Federated Learning	—Unverified
Energy-Aware Analog Aggregation for Federated Learning with Redundant Data	Nov 1, 2019	Federated LearningScheduling	—Unverified
Energy-Aware Edge Association for Cluster-based Personalized Federated Learning	Feb 6, 2022	Deep Reinforcement LearningFederated Learning	—Unverified
Energy-Aware Federated Learning in Satellite Constellations	Sep 23, 2024	Federated LearningManagement	—Unverified
Energy-Aware Federated Learning with Distributed User Sampling and Multichannel ALOHA	Sep 12, 2023	Federated Learning	—Unverified
Energy Demand Prediction with Federated Learning for Electric Vehicle Networks	Sep 3, 2019	BIG-bench Machine LearningClustering	—Unverified
Energy-Efficient Channel Decoding for Wireless Federated Learning: Convergence Analysis and Adaptive Design	Jun 26, 2024	Federated Learning	—Unverified
Energy-Efficient Federated Edge Learning with Streaming Data: A Lyapunov Optimization Approach	May 20, 2024	Federated LearningScheduling	—Unverified
Energy-Efficient Federated Learning and Migration in Digital Twin Edge Networks	Mar 20, 2025	Federated Learning	—Unverified
Energy Efficient Federated Learning in Integrated Fog-Cloud Computing Enabled Internet-of-Things Networks	Jul 7, 2021	Cloud ComputingFederated Learning	—Unverified
Energy-Efficient Federated Learning in Cooperative Communication within Factory Subnetworks	Apr 27, 2024	Federated Learning	—Unverified
Energy Efficient Federated Learning Over Wireless Communication Networks	Nov 6, 2019	Federated Learning	—Unverified
Energy Minimization for Participatory Federated Learning in IoT Analyzed via Game Theory	Mar 27, 2025	Decision MakingFederated Learning	—Unverified
Enforcing fairness in private federated learning via the modified method of differential multipliers	Sep 17, 2021	BIG-bench Machine LearningFairness	—Unverified
Enhanced channel estimation for near-field IRS-aided multi-user MIMO system via a large deep residual network	Oct 28, 2024	DenoisingFederated Learning	—Unverified
Enhanced Decentralized Federated Learning based on Consensus in Connected Vehicles	Sep 22, 2022	Decision MakingFederated Learning	—Unverified
Enhanced Federated Anomaly Detection Through Autoencoders Using Summary Statistics-Based Thresholding	Oct 11, 2024	Anomaly DetectionFederated Learning	—Unverified
Enhance Local Consistency in Federated Learning: A Multi-Step Inertial Momentum Approach	Feb 11, 2023	Edge-computingFederated Learning	—Unverified
Enhanced Over-the-Air Federated Learning Using AI-based Fluid Antenna System	Jul 3, 2024	Federated LearningPosition	—Unverified
Enhancing Air Quality Monitoring: A Brief Review of Federated Learning Advances	Apr 3, 2025	Federated Learning	—Unverified
Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling	Jun 3, 2025	FairnessFederated Learning	—Unverified
Enhancing Data Provenance and Model Transparency in Federated Learning Systems - A Database Approach	Mar 3, 2024	Federated Learning	—Unverified
Enhancing Equitable Access to AI in Housing and Homelessness System of Care through Federated Learning	Aug 14, 2024	Federated Learning	—Unverified
Enhancing Federated Domain Adaptation with Multi-Domain Prototype-Based Federated Fine-Tuning	Oct 10, 2024	Domain AdaptationFederated Learning	—Unverified

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All datasets Cityscapes heterogeneous Landmarks-User-160k CIFAR-100 (alpha=0, 10 clients per round)CIFAR-100 (alpha=0, 20 clients per round)CIFAR-100 (alpha=0.5, 10 clients per round)CIFAR-100 (alpha=0.5, 20 clients per round)CIFAR-100 (alpha=0.5, 5 clients per round)CIFAR-100 (alpha=0, 5 clients per round)CIFAR-100 (alpha=1000, 10 clients per round)CIFAR-100 (alpha=1000, 20 clients per round)CIFAR-100 (alpha=1000, 5 clients per round)CIFAR100 (alpha=0.3, 10 clients per round)

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	SiloBN + ASAM	mIoU	49.75	—	Unverified
2	SiloBN + SAM	mIoU	49.1	—	Unverified
3	SiloBN	mIoU	45.96	—	Unverified
4	FedSAM + SWA	mIoU	43.42	—	Unverified
5	FedASAM + SWA	mIoU	43.02	—	Unverified
6	FedAvg + SWA	mIoU	42.48	—	Unverified
7	FedASAM	mIoU	42.27	—	Unverified
8	FedSAM	mIoU	41.22	—	Unverified
9	FedAvg	mIoU	38.65	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	Acc@1-1262Clients	68.32	—	Unverified
2	FedSAM + SWA	Acc@1-1262Clients	68.12	—	Unverified
3	FedAvg + SWA	Acc@1-1262Clients	67.52	—	Unverified
4	FedASAM	Acc@1-1262Clients	64.23	—	Unverified
5	FedSAM	Acc@1-1262Clients	63.72	—	Unverified
6	FedAvg	Acc@1-1262Clients	61.91	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	ACC@1-100Clients	42.64	—	Unverified
2	FedASAM	ACC@1-100Clients	39.76	—	Unverified
3	FedSAM + SWA	ACC@1-100Clients	39.51	—	Unverified
4	FedSAM	ACC@1-100Clients	36.93	—	Unverified
5	FedAvg	ACC@1-100Clients	36.74	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	ACC@1-100Clients	41.62	—	Unverified
2	FedASAM	ACC@1-100Clients	40.81	—	Unverified
3	FedSAM + SWA	ACC@1-100Clients	39.24	—	Unverified
4	FedAvg	ACC@1-100Clients	38.59	—	Unverified
5	FedSAM	ACC@1-100Clients	38.56	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	ACC@1-100Clients	48.72	—	Unverified
2	FedSAM + SWA	ACC@1-100Clients	46.76	—	Unverified
3	FedASAM	ACC@1-100Clients	46.58	—	Unverified
4	FedSAM	ACC@1-100Clients	44.84	—	Unverified
5	FedAvg	ACC@1-100Clients	41.27	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	ACC@1-100Clients	48.27	—	Unverified
2	FedASAM	ACC@1-100Clients	47.78	—	Unverified
3	FedSAM + SWA	ACC@1-100Clients	46.47	—	Unverified
4	FedSAM	ACC@1-100Clients	46.05	—	Unverified
5	FedAvg	ACC@1-100Clients	42.17	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	ACC@1-100Clients	49.17	—	Unverified
2	FedSAM + SWA	ACC@1-100Clients	47.96	—	Unverified
3	FedASAM	ACC@1-100Clients	45.61	—	Unverified
4	FedSAM	ACC@1-100Clients	44.73	—	Unverified
5	FedAvg	ACC@1-100Clients	40.43	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM + SWA	ACC@1-100Clients	42.01	—	Unverified
2	FedSAM + SWA	ACC@1-100Clients	39.3	—	Unverified
3	FedASAM	ACC@1-100Clients	36.04	—	Unverified
4	FedSAM	ACC@1-100Clients	31.04	—	Unverified
5	FedAvg	ACC@1-100Clients	30.25	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM	ACC@1-100Clients	54.97	—	Unverified
2	FedASAM + SWA	ACC@1-100Clients	54.79	—	Unverified
3	FedSAM + SWA	ACC@1-100Clients	53.67	—	Unverified
4	FedSAM	ACC@1-100Clients	53.39	—	Unverified
5	FedAvg	ACC@1-100Clients	50.25	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM	ACC@1-100Clients	54.5	—	Unverified
2	FedSAM + SWA	ACC@1-100Clients	54.36	—	Unverified
3	FedASAM + SWA	ACC@1-100Clients	54.1	—	Unverified
4	FedSAM	ACC@1-100Clients	53.97	—	Unverified
5	FedAvg	ACC@1-100Clients	50.66	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FedASAM	ACC@1-100Clients	54.81	—	Unverified
2	FedSAM	ACC@1-100Clients	54.01	—	Unverified
3	FedSAM + SWA	ACC@1-100Clients	53.9	—	Unverified
4	FedASAM + SWA	ACC@1-100Clients	53.86	—	Unverified
5	FedAvg	ACC@1-100Clients	49.92	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	AdaBest	Average Top-1 Accuracy	56.2	—	Unverified