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 4751–4775 of 6771 papers

Title	Date	Tasks	Status
Hierarchical Federated Learning through LAN-WAN Orchestration	Oct 22, 2020	Federated Learning	—Unverified
Hierarchical Federated Learning with Momentum Acceleration in Multi-Tier Networks	Oct 26, 2022	Federated Learning	—Unverified
Hierarchical Federated Learning with Privacy	Jun 10, 2022	Federated Learning	—Unverified
Hierarchical Knowledge Structuring for Effective Federated Learning in Heterogeneous Environments	Apr 4, 2025	Federated Learning	—Unverified
Hierarchically Fair Federated Learning	Apr 22, 2020	FairnessFederated Learning	—Unverified
Hierarchical Over-the-Air Federated Edge Learning	Dec 21, 2021	Federated Learning	—Unverified
Hierarchical Over-the-Air Federated Learning with Awareness of Interference and Data Heterogeneity	Jan 2, 2024	Federated Learning	—Unverified
Hierarchical Over-the-Air FedGradNorm	Dec 14, 2022	Federated LearningMulti-Task Learning	—Unverified
Hierarchical Personalized Federated Learning Over Massive Mobile Edge Computing Networks	Mar 19, 2023	Edge-computingFederated Learning	—Unverified
Hierarchical Federated Learning with Quantization: Convergence Analysis and System Design	Mar 26, 2021	Federated LearningQuantization	—Unverified
Hierarchical Split Federated Learning: Convergence Analysis and System Optimization	Dec 10, 2024	Federated Learning	—Unverified
HierarchyFL: Heterogeneous Federated Learning via Hierarchical Self-Distillation	Dec 5, 2022	DiversityFederated Learning	—Unverified
HierSFL: Local Differential Privacy-aided Split Federated Learning in Mobile Edge Computing	Jan 16, 2024	Edge-computingFederated Learning	—Unverified
HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Association	Jan 16, 2023	Deep Reinforcement LearningEdge-computing	—Unverified
High-Dimensional Stochastic Gradient Quantization for Communication-Efficient Edge Learning	Oct 9, 2019	Federated LearningQuantization	—Unverified
Hijack Vertical Federated Learning Models As One Party	Dec 1, 2022	Federated LearningVertical Federated Learning	—Unverified
Hire When You Need to: Gradual Participant Recruitment for Auction-based Federated Learning	Oct 4, 2023	DiversityFederated Learning	—Unverified
HistoFS: Non-IID Histopathologic Whole Slide Image Classification via Federated Style Transfer with RoI-Preserving	Jan 1, 2025	Federated Learningimage-classification	—Unverified
Histogram-Based Federated XGBoost using Minimal Variance Sampling for Federated Tabular Data	May 3, 2024	Federated Learning	—Unverified
Histopathological Image Classification and Vulnerability Analysis using Federated Learning	Oct 11, 2023	ClassificationData Poisoning	—Unverified
A Historical Trajectory Assisted Optimization Method for Zeroth-Order Federated Learning	Sep 24, 2024	Federated Learning	—Unverified
HLF-FSL. A Decentralized Federated Split Learning Solution for IoT on Hyperledger Fabric	Jul 10, 2025	Federated LearningPrivacy Preserving	—Unverified
Holdout SGD: Byzantine Tolerant Federated Learning	Aug 11, 2020	Federated Learning	—Unverified
Holistic Evaluation Metrics: Use Case Sensitive Evaluation Metrics for Federated Learning	May 3, 2024	Computational EfficiencyFairness	—Unverified
HoloFed: Environment-Adaptive Positioning via Multi-band Reconfigurable Holographic Surfaces and Federated Learning	Oct 10, 2023	Federated LearningPosition	—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