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 1876–1900 of 6771 papers

Title	Date	Tasks	Status
Efficient Ring-topology Decentralized Federated Learning with Deep Generative Models for Industrial Artificial Intelligent	Apr 15, 2021	Federated Learning	—Unverified
DAG-ACFL: Asynchronous Clustered Federated Learning based on DAG-DLT	Aug 25, 2023	Change Point DetectionFederated Learning	—Unverified
DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning	Oct 31, 2021	Federated LearningPrivacy Preserving	—Unverified
Efficient Training of Large-Scale AI Models Through Federated Mixture-of-Experts: A System-Level Approach	Jul 8, 2025	Edge-computingFederated Learning	—Unverified
Efficient Training of Large-scale Industrial Fault Diagnostic Models through Federated Opportunistic Block Dropout	Feb 22, 2023	DiagnosticFederated Learning	—Unverified
Efficient Transmission of Radiomaps via Physics-Enhanced Semantic Communications	Jan 18, 2025	Edge-computingFederated Learning	—Unverified
Efficient UAV Swarm-Based Multi-Task Federated Learning with Dynamic Task Knowledge Sharing	Mar 12, 2025	Federated Learning	—Unverified
Efficient Unbiased Sparsification	Feb 22, 2024	Federated Learning	—Unverified
Byzantine-robust Federated Learning through Spatial-temporal Analysis of Local Model Updates	Jul 3, 2021	Federated Learning	—Unverified
Efficient Vertical Federated Learning with Secure Aggregation	May 18, 2023	Federated LearningFraud Detection	—Unverified
Efficient Zero-Order Federated Finetuning of Language Models for Resource-Constrained Devices	Feb 14, 2025	Federated Learning	—Unverified
DACFL: Dynamic Average Consensus Based Federated Learning in Decentralized Topology	Nov 10, 2021	Federated Learning	—Unverified
EFMVFL: An Efficient and Flexible Multi-party Vertical Federated Learning without a Third Party	Jan 17, 2022	Federated Learningregression	—Unverified
Adaptive Decentralized Federated Learning in Energy and Latency Constrained Wireless Networks	Mar 29, 2024	Federated Learning	—Unverified
Elastic Aggregation for Federated Optimization	Jan 1, 2023	Federated LearningPrivacy Preserving	—Unverified
Elastically-Constrained Meta-Learner for Federated Learning	Jun 29, 2023	Federated LearningMeta-Learning	—Unverified
Election of Collaborators via Reinforcement Learning for Federated Brain Tumor Segmentation	Dec 28, 2024	Brain Tumor SegmentationFederated Learning	—Unverified
Electrical Load Forecasting in Smart Grid: A Personalized Federated Learning Approach	Nov 15, 2024	Federated LearningLoad Forecasting	—Unverified
Electrical Load Forecasting over Multihop Smart Metering Networks with Federated Learning	Feb 24, 2025	Federated LearningLoad Forecasting	—Unverified
Accelerating Split Federated Learning over Wireless Communication Networks	Oct 24, 2023	Federated Learning	—Unverified
Enhancing Quantum Security over Federated Learning via Post-Quantum Cryptography	Sep 6, 2024	Federated Learning	—Unverified
Enhancing Security and Privacy in Federated Learning using Low-Dimensional Update Representation and Proximity-Based Defense	May 29, 2024	Federated LearningPrivacy Preserving	—Unverified
Embedded Federated Feature Selection with Dynamic Sparse Training: Balancing Accuracy-Cost Tradeoffs	Apr 7, 2025	Computational Efficiencyfeature selection	—Unverified
Embedding Alignment for Unsupervised Federated Learning via Smart Data Exchange	Aug 4, 2022	Contrastive LearningFederated Learning	—Unverified
DABS: Data-Agnostic Backdoor attack at the Server in Federated Learning	May 2, 2023	Backdoor AttackFederated Learning	—Unverified

Show:10 25 50

← PrevPage 76 of 271Next →

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