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 1951–1975 of 6771 papers

Title	Date	Tasks	Status
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
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
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
SHED: A Newton-type algorithm for federated learning based on incremental Hessian eigenvector sharing	Feb 11, 2022	Distributed OptimizationFederated Learning	—Unverified
Towards cost-effective and resource-aware aggregation at Edge for Federated Learning	Apr 16, 2022	Federated 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
Efficient Ring-topology Decentralized Federated Learning with Deep Generative Models for Industrial Artificial Intelligent	Apr 15, 2021	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
Enhancing Federated Learning Convergence with Dynamic Data Queue and Data Entropy-driven Participant Selection	Oct 23, 2024	Federated Learning	—Unverified
Efficient Reinforcement Learning in Resource Allocation Problems Through Permutation Invariant Multi-task Learning	Feb 18, 2021	Federated LearningMulti-Task Learning	—Unverified
Enhancing Federated Learning with Adaptive Differential Privacy and Priority-Based Aggregation	Jun 26, 2024	Federated Learning	—Unverified
Byzantine-Robust Decentralized Federated Learning	Jun 14, 2024	Federated Learning	—Unverified
Enhancing Federated Survival Analysis through Peer-Driven Client Reputation in Healthcare	May 22, 2025	Federated LearningSurvival Analysis	—Unverified
Friends in Unexpected Places: Enhancing Local Fairness in Federated Learning through Clustering	Jul 27, 2024	ClusteringFairness	—Unverified
Enhancing Intrusion Detection In Internet Of Vehicles Through Federated Learning	Nov 23, 2023	Federated LearningIntrusion Detection	—Unverified
A New Theoretical Perspective on Data Heterogeneity in Federated Optimization	Jul 22, 2024	Federated Learning	—Unverified
Enhancing Mental Health Support through Human-AI Collaboration: Toward Secure and Empathetic AI-enabled chatbots	Sep 17, 2024	ChatbotFederated Learning	—Unverified
Enhancing Mutual Trustworthiness in Federated Learning for Data-Rich Smart Cities	May 1, 2024	Federated LearningPrivacy Preserving	—Unverified
Enhancing Neural Training via a Correlated Dynamics Model	Dec 20, 2023	Federated Learningimage-classification	—Unverified
Efficient Privacy Preserving Edge Computing Framework for Image Classification	May 10, 2020	ClassificationData Compression	—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