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 851–900 of 6771 papers

Title	Date	Tasks	Status
A Comparative Evaluation of FedAvg and Per-FedAvg Algorithms for Dirichlet Distributed Heterogeneous Data	Sep 3, 2023	Federated Learning	—Unverified
Load Balancing in Federated Learning	Aug 1, 2024	Decision MakingFairness	—Unverified
Anomalous Client Detection in Federated Learning	Nov 3, 2024	Anomaly DetectionEdge-computing	—Unverified
AnoFel: Supporting Anonymity for Privacy-Preserving Federated Learning	Jun 12, 2023	Federated LearningPrivacy Preserving	—Unverified
Advanced Deep Learning and Large Language Models: Comprehensive Insights for Cancer Detection	Mar 30, 2025	DiagnosticFederated Learning	—Unverified
FLBench: A Benchmark Suite for Federated Learning	Aug 17, 2020	BIG-bench Machine LearningFederated Learning	—Unverified
An Investigation towards Differentially Private Sequence Tagging in a Federated Framework	Jun 1, 2021	Federated Learningnamed-entity-recognition	—Unverified
How global observation works in Federated Learning: Integrating vertical training into Horizontal Federated Learning	Dec 2, 2021	Federated Learning	—Unverified
A cautionary tale on the cost-effectiveness of collaborative AI in real-world medical applications	Dec 9, 2024	Federated Learning	—Unverified
An Interpretable Federated Learning-based Network Intrusion Detection Framework	Jan 10, 2022	Federated LearningIntrusion Detection	—Unverified
AdRo-FL: Informed and Secure Client Selection for Federated Learning in the Presence of Adversarial Aggregator	Jun 21, 2025	Federated LearningPrivacy Preserving	—Unverified
Conditioning on Local Statistics for Scalable Heterogeneous Federated Learning	Mar 1, 2025	Federated Learning	—Unverified
A(DP)^2SGD: Asynchronous Decentralized Parallel Stochastic Gradient Descent with Differential Privacy	Aug 21, 2020	Federated Learning	—Unverified
An Intelligent Native Network Slicing Security Architecture Empowered by Federated Learning	Oct 4, 2024	Federated Learning	—Unverified
A Carbon Tracking Model for Federated Learning: Impact of Quantization and Sparsification	Oct 12, 2023	Federated LearningQuantization	—Unverified
Chu-ko-nu: A Reliable, Efficient, and Anonymously Authentication-Enabled Realization for Multi-Round Secure Aggregation in Federated Learning	Feb 23, 2024	Federated Learning	—Unverified
An Intelligent Mechanism for Monitoring and Detecting Intrusions in IoT Devices	Jun 23, 2023	Federated LearningIntrusion Detection	—Unverified
An Innovative Networks in Federated Learning	May 28, 2024	Computational EfficiencyFederated Learning	—Unverified
ADMarker: A Multi-Modal Federated Learning System for Monitoring Digital Biomarkers of Alzheimer's Disease	Oct 23, 2023	Federated LearningPrivacy Preserving	—Unverified
An Information Theoretic Perspective on Conformal Prediction	May 3, 2024	Conformal PredictionFederated Learning	—Unverified
An Information-Theoretic Analysis of The Cost of Decentralization for Learning and Inference Under Privacy Constraints	Oct 11, 2021	Federated LearningVertical Federated Learning	—Unverified
A Communication-Efficient Adaptive Algorithm for Federated Learning under Cumulative Regret	Jan 21, 2023	Federated LearningStochastic Optimization	—Unverified
Citadel: Protecting Data Privacy and Model Confidentiality for Collaborative Learning with SGX	May 4, 2021	Federated Learning	—Unverified
Client-Centric Federated Adaptive Optimization	Jan 17, 2025	Federated Learning	—Unverified
An Information-Theoretic Analysis for Federated Learning under Concept Drift	Jun 26, 2025	Federated Learning	—Unverified
An Incentive Mechanism for Federated Learning Based on Multiple Resource Exchange	Dec 13, 2023	Federated Learning	—Unverified
A Communication and Computation Efficient Fully First-order Method for Decentralized Bilevel Optimization	Oct 18, 2024	Bilevel OptimizationFederated Learning	—Unverified
An Incentive Mechanism for Federated Learning in Wireless Cellular network: An Auction Approach	Sep 22, 2020	BIG-bench Machine LearningFederated Learning	—Unverified
A Distributed Cubic-Regularized Newton Method for Smooth Convex Optimization over Networks	Jul 7, 2020	Federated Learning	—Unverified
Protecting Federated Learning from Extreme Model Poisoning Attacks via Multidimensional Time Series Anomaly Detection	Mar 29, 2023	Anomaly DetectionFederated Learning	—Unverified
Channel and Gradient-Importance Aware Device Scheduling for Over-the-Air Federated Learning	May 26, 2023	Federated LearningPrivacy Preserving	—Unverified
An Experimental Study of Data Heterogeneity in Federated Learning Methods for Medical Imaging	Jul 18, 2021	Federated LearningPrivacy Preserving	—Unverified
An Experimental Study of Class Imbalance in Federated Learning	Sep 9, 2021	BIG-bench Machine LearningFederated Learning	—Unverified
A Distributed Computation Model Based on Federated Learning Integrates Heterogeneous models and Consortium Blockchain for Solving Time-Varying Problems	Jun 28, 2023	Federated Learning	—Unverified
An Experimental Study of Byzantine-Robust Aggregation Schemes in Federated Learning	Feb 14, 2023	Federated Learning	—Unverified
An Expectation-Maximization Perspective on Federated Learning	Nov 19, 2021	Federated Learning	—Unverified
A Distributed Approach to Meteorological Predictions: Addressing Data Imbalance in Precipitation Prediction Models through Federated Learning and GANs	Oct 19, 2023	Data AugmentationFederated Learning	—Unverified
A collaborative ensemble construction method for federated random forest	Jul 27, 2024	Federated Learning	—Unverified
Channel-driven Decentralized Bayesian Federated Learning for Trustworthy Decision Making in D2D Networks	Oct 19, 2022	Decision MakingFederated Learning	—Unverified
A new type of federated clustering: A non-model-sharing approach	Jun 11, 2025	ClusteringFederated 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
A New Theoretical Perspective on Data Heterogeneity in Federated Optimization	Jul 22, 2024	Federated Learning	—Unverified
A distillation-based approach integrating continual learning and federated learning for pervasive services	Sep 9, 2021	Activity RecognitionContinual Learning	—Unverified
A Coalition Formation Game Approach for Personalized Federated Learning	Feb 5, 2022	DiversityFederated Learning	—Unverified
Challenges and Opportunities for Machine Learning Classification of Behavior and Mental State from Images	Jan 26, 2022	Active LearningBIG-bench Machine Learning	—Unverified
A Differentially Private Probabilistic Framework for Modeling the Variability Across Federated Datasets of Heterogeneous Multi-View Observations	Apr 15, 2022	Federated Learning	—Unverified
A New Implementation of Federated Learning for Privacy and Security Enhancement	Aug 3, 2022	Federated Learning	—Unverified
PA-CFL: Privacy-Adaptive Clustered Federated Learning for Transformer-Based Sales Forecasting on Heterogeneous Retail Data	Mar 15, 2025	Demand ForecastingFeature Importance	—Unverified
A new framework for prognostics in decentralized industries: Enhancing fairness, security, and transparency through Blockchain and Federated Learning	Feb 17, 2025	FairnessFederated 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