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 1851–1875 of 6771 papers

Title	Date	Tasks	Status
Can collaborative learning be private, robust and scalable?	May 5, 2022	Adversarial RobustnessFederated Learning	—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
Cali3F: Calibrated Fast Fair Federated Recommendation System	May 26, 2022	FairnessFederated Learning	—Unverified
An Information Theoretic Perspective on Conformal Prediction	May 3, 2024	Conformal PredictionFederated Learning	—Unverified
CAFe: Cost and Age aware Federated Learning	May 24, 2024	Federated 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
A Carbon Tracking Model for Federated Learning: Impact of Quantization and Sparsification	Oct 12, 2023	Federated LearningQuantization	—Unverified
Conditioning on Local Statistics for Scalable Heterogeneous Federated Learning	Mar 1, 2025	Federated Learning	—Unverified
Load Balancing in Federated Learning	Aug 1, 2024	Decision MakingFairness	—Unverified
CAFE: Catastrophic Data Leakage in Federated Learning	Jan 1, 2021	Federated Learning	—Unverified
An Information-Theoretic Analysis for Federated Learning under Concept Drift	Jun 26, 2025	Federated Learning	—Unverified
CAFE: Carbon-Aware Federated Learning in Geographically Distributed Data Centers	Nov 6, 2023	Federated Learning	—Unverified
Caesar: A Low-deviation Compression Approach for Efficient Federated Learning	Dec 28, 2024	Federated Learning	—Unverified
An Incentive Mechanism for Federated Learning Based on Multiple Resource Exchange	Dec 13, 2023	Federated Learning	—Unverified
CADRE: Customizable Assurance of Data Readiness in Privacy-Preserving Federated Learning	May 28, 2025	FairnessFederated Learning	—Unverified
An Incentive Mechanism for Federated Learning in Wireless Cellular network: An Auction Approach	Sep 22, 2020	BIG-bench Machine LearningFederated Learning	—Unverified
CaBaFL: Asynchronous Federated Learning via Hierarchical Cache and Feature Balance	Apr 19, 2024	Federated Learning	—Unverified
A Distributed Cubic-Regularized Newton Method for Smooth Convex Optimization over Networks	Jul 7, 2020	Federated Learning	—Unverified
A Communication and Computation Efficient Fully First-order Method for Decentralized Bilevel Optimization	Oct 18, 2024	Bilevel OptimizationFederated Learning	—Unverified
Byzantines can also Learn from History: Fall of Centered Clipping in Federated Learning	Aug 21, 2022	Federated Learningimage-classification	—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

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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