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 1001–1025 of 6771 papers

Title	Date	Tasks	Status	Score
Central Server Free Federated Learning over Single-sided Trust Social Networks	Oct 11, 2019	Federated Learning	CodeCode Available	5
Framework for Co-distillation Driven Federated Learning to Address Class Imbalance in Healthcare	Nov 15, 2024	Federated Learning	CodeCode Available	5
Federated Representation Learning in the Under-Parameterized Regime	Jun 7, 2024	Federated LearningPersonalized Federated Learning	CodeCode Available	5
Freezing of Gait Detection Using Gramian Angular Fields and Federated Learning from Wearable Sensors	Nov 18, 2024	Federated LearningMissing Values	CodeCode Available	5
From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning	Feb 24, 2023	Federated Learning	CodeCode Available	5
Federated Spectral Graph Transformers Meet Neural Ordinary Differential Equations for Non-IID Graphs	Apr 16, 2025	Federated LearningFraud Detection	CodeCode Available	5
Federated Prediction-Powered Inference from Decentralized Data	Sep 3, 2024	Federated LearningPrediction	CodeCode Available	5
Federated Over-Air Subspace Tracking from Incomplete and Corrupted Data	Feb 28, 2020	Federated Learning	CodeCode Available	5
A Novel Defense Against Poisoning Attacks on Federated Learning: LayerCAM Augmented with Autoencoder	Jun 2, 2024	Federated LearningModel Poisoning	CodeCode Available	5
FedPCL-CDR: A Federated Prototype-based Contrastive Learning Framework for Privacy-Preserving Cross-domain Recommendation	Sep 5, 2024	Contrastive LearningFederated Learning	CodeCode Available	5
Federated Optimization for Heterogeneous Networks	May 16, 2019	Federated LearningMulti-Task Learning	CodeCode Available	5
Federated Noisy Client Learning	Jun 24, 2021	Federated Learning	CodeCode Available	5
Federated Nearest Neighbor Classification with a Colony of Fruit-Flies: With Supplement	Dec 14, 2021	Federated LearningOutlier Detection	CodeCode Available	5
Generalizable Learning Reconstruction for Accelerating MR Imaging via Federated Neural Architecture Search	Aug 27, 2023	Efficient Neural NetworkFairness	CodeCode Available	5
Masked Random Noise for Communication Efficient Federated Learning	Aug 6, 2024	Federated Learning	CodeCode Available	5
Federated Neural Radiance Fields	May 2, 2023	Federated LearningNeRF	CodeCode Available	5
A Novel Optimized Asynchronous Federated Learning Framework	Nov 18, 2021	Federated Learning	CodeCode Available	5
Federated Stain Normalization for Computational Pathology	Sep 29, 2022	DiversityFederated Learning	CodeCode Available	5
FedFTN: Personalized Federated Learning with Deep Feature Transformation Network for Multi-institutional Low-count PET Denoising	Apr 2, 2023	DenoisingFederated Learning	CodeCode Available	5
Federated Multimodal Learning with Dual Adapters and Selective Pruning for Communication and Computational Efficiency	Mar 10, 2025	Computational EfficiencyFederated Learning	CodeCode Available	5
Federated Multi-armed Bandits with Personalization	Feb 25, 2021	Federated LearningMulti-Armed Bandits	CodeCode Available	5
Client Adaptation improves Federated Learning with Simulated Non-IID Clients	Jul 9, 2020	Federated Learning	CodeCode Available	5
Federated Multi-Task Learning	May 30, 2017	BIG-bench Machine LearningFederated Learning	CodeCode Available	5
An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects	Jan 1, 2023	Causal InferenceFederated Learning	CodeCode Available	5
Federated Machine Learning: Concept and Applications	Feb 13, 2019	BIG-bench Machine LearningFederated Learning	CodeCode Available	5

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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