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 1301–1325 of 6771 papers

Title	Date	Tasks	Status
Backdoor Defense in Federated Learning Using Differential Testing and Outlier Detection	Feb 21, 2022	backdoor defenseFederated Learning	—Unverified
Backdoor Attacks on Federated Meta-Learning	Jun 12, 2020	Federated LearningMeta-Learning	—Unverified
AIGC-assisted Federated Learning for Edge Intelligence: Architecture Design, Research Challenges and Future Directions	Mar 26, 2025	Federated Learning	—Unverified
AdaptiveFL: Adaptive Heterogeneous Federated Learning for Resource-Constrained AIoT Systems	Nov 22, 2023	Federated Learning	—Unverified
Backdoor Attacks in Peer-to-Peer Federated Learning	Jan 23, 2023	Backdoor AttackFederated Learning	—Unverified
AI-Driven IRM: Transforming insider risk management with adaptive scoring and LLM-based threat detection	May 1, 2025	Anomaly DetectionFederated Learning	—Unverified
Backdoor Attacks in Federated Learning by Rare Embeddings and Gradient Ensembling	Apr 29, 2022	Federated LearningModel Poisoning	—Unverified
Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directions	Mar 3, 2023	Backdoor AttackFederated Learning	—Unverified
AIDRIN 2.0: A Framework to Assess Data Readiness for AI	May 22, 2025	FairnessFederated Learning	—Unverified
Adaptive Federated Pruning in Hierarchical Wireless Networks	May 15, 2023	Federated LearningPrivacy Preserving	—Unverified
Accuracy and Privacy Evaluations of Collaborative Data Analysis	Jan 27, 2021	Dimensionality ReductionFederated Learning	—Unverified
Backdoor attacks and defenses in feature-partitioned collaborative learning	Jul 7, 2020	Federated Learning	—Unverified
Backdoor Attack on Vertical Federated Graph Neural Network Learning	Oct 15, 2024	Backdoor AttackFederated Learning	—Unverified
Aiding Global Convergence in Federated Learning via Local Perturbation and Mutual Similarity Information	Oct 7, 2024	Distributed OptimizationFederated Learning	—Unverified
Backdoor Attack and Defense in Federated Generative Adversarial Network-based Medical Image Synthesis	Oct 19, 2022	Backdoor AttackData Augmentation	—Unverified
AI-based traffic analysis in digital twin networks	Nov 1, 2024	FairnessFederated Learning	—Unverified
A Bayesian Federated Learning Framework with Online Laplace Approximation	Feb 3, 2021	Federated Learning	—Unverified
Information-Geometric Barycenters for Bayesian Federated Learning	Dec 16, 2024	Bayesian InferenceDistributed Optimization	—Unverified
B^2SFL: A Bi-level Blockchained Architecture for Secure Federated Learning-based Traffic Prediction	Oct 23, 2023	Federated LearningPrediction	—Unverified
AI-Based Crypto Tokens: The Illusion of Decentralized AI?	Apr 29, 2025	Federated Learning	—Unverified
A Weighted Loss Approach to Robust Federated Learning under Data Heterogeneity	Jun 11, 2025	Federated Learning	—Unverified
AI Approaches in Processing and Using Data in Personalized Medicine	Jul 26, 2022	Explainable artificial intelligenceFederated Learning	—Unverified
Adaptive Federated Minimax Optimization with Lower Complexities	Nov 14, 2022	Federated LearningPrivacy Preserving	—Unverified
Decentralized Federated Learning: A Survey and Perspective	Jun 2, 2023	Federated LearningSurvey	—Unverified
Decentralized Federated Learning: A Survey on Security and Privacy	Jan 25, 2024	Federated LearningPrivacy Preserving	—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