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 1926–1950 of 6771 papers

Title	Date	Tasks	Status
Enabling Trustworthy Federated Learning in Industrial IoT: Bridging the Gap Between Interpretability and Robustness	Sep 1, 2024	Decision MakingFederated Learning	—Unverified
Efficient Unbiased Sparsification	Feb 22, 2024	Federated Learning	—Unverified
EncCluster: Scalable Functional Encryption in Federated Learning through Weight Clustering and Probabilistic Filters	Jun 13, 2024	Federated LearningModel Compression	—Unverified
Encoded Spatial Attribute in Multi-Tier Federated Learning	Jan 10, 2025	AttributeFederated Learning	—Unverified
Byzantine-robust Federated Learning through Spatial-temporal Analysis of Local Model Updates	Jul 3, 2021	Federated Learning	—Unverified
A new type of federated clustering: A non-model-sharing approach	Jun 11, 2025	ClusteringFederated Learning	—Unverified
End-to-End on-device Federated Learning: A case study	Jan 1, 2021	Autonomous DrivingBIG-bench Machine Learning	—Unverified
Efficient UAV Swarm-Based Multi-Task Federated Learning with Dynamic Task Knowledge Sharing	Mar 12, 2025	Federated Learning	—Unverified
Efficient Transmission of Radiomaps via Physics-Enhanced Semantic Communications	Jan 18, 2025	Edge-computingFederated Learning	—Unverified
Byzantine-Robust Federated Learning over Ring-All-Reduce Distributed Computing	Jan 29, 2025	AllDistributed Computing	—Unverified
Efficient Training of Large-scale Industrial Fault Diagnostic Models through Federated Opportunistic Block Dropout	Feb 22, 2023	DiagnosticFederated Learning	—Unverified
Efficient Training of Large-Scale AI Models Through Federated Mixture-of-Experts: A System-Level Approach	Jul 8, 2025	Edge-computingFederated Learning	—Unverified
Energy-Aware Edge Association for Cluster-based Personalized Federated Learning	Feb 6, 2022	Deep Reinforcement LearningFederated Learning	—Unverified
Byzantine-Robust Federated Learning: An Overview With Focus on Developing Sybil-based Attacks to Backdoor Augmented Secure Aggregation Protocols	Oct 30, 2024	Federated Learning	—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
SHED: A Newton-type algorithm for federated learning based on incremental Hessian eigenvector sharing	Feb 11, 2022	Distributed OptimizationFederated Learning	—Unverified
Energy-Efficient Federated Learning and Migration in Digital Twin Edge Networks	Mar 20, 2025	Federated Learning	—Unverified
Towards cost-effective and resource-aware aggregation at Edge for Federated Learning	Apr 16, 2022	Federated Learning	—Unverified
Efficient Ring-topology Decentralized Federated Learning with Deep Generative Models for Industrial Artificial Intelligent	Apr 15, 2021	Federated 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
Efficient Reinforcement Learning in Resource Allocation Problems Through Permutation Invariant Multi-task Learning	Feb 18, 2021	Federated LearningMulti-Task Learning	—Unverified
Byzantine-Robust Decentralized Federated Learning	Jun 14, 2024	Federated 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