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 6676–6700 of 6771 papers

Title	Date	Tasks	Status
Infinitely Divisible Noise in the Low Privacy Regime	Oct 13, 2021	Federated LearningPrivacy Preserving	CodeCode Available
A Federated Data-Driven Evolutionary Algorithm for Expensive Multi/Many-objective Optimization	Jun 22, 2021	Evolutionary AlgorithmsFederated Learning	CodeCode Available
InFL-UX: A Toolkit for Web-Based Interactive Federated Learning	Mar 6, 2025	Federated Learning	CodeCode Available
On the Convergence of Decentralized Federated Learning Under Imperfect Information Sharing	Mar 19, 2023	Distributed OptimizationFederated Learning	CodeCode Available
DROP: Poison Dilution via Knowledge Distillation for Federated Learning	Feb 10, 2025	Data PoisoningFederated Learning	CodeCode Available
A Unified Solution to Diverse Heterogeneities in One-shot Federated Learning	Oct 28, 2024	Federated Learning	CodeCode Available
On the Convergence of Federated Learning Algorithms without Data Similarity	Feb 29, 2024	Federated Learning	CodeCode Available
Informed Greedy Algorithm for Scalable Bayesian Network Fusion via Minimum Cut Analysis	Apr 1, 2025	Federated Learning	CodeCode Available
A Method to Reveal Speaker Identity in Distributed ASR Training, and How to Counter It	Apr 15, 2021	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	CodeCode Available
A Lightweight and Secure Deep Learning Model for Privacy-Preserving Federated Learning in Intelligent Enterprises	Mar 3, 2025	Federated LearningPrivacy Preserving	CodeCode Available
Robust Federated Learning Against Poisoning Attacks: A GAN-Based Defense Framework	Mar 26, 2025	Federated LearningGenerative Adversarial Network	CodeCode Available
Robust Federated Learning by Mixture of Experts	Apr 23, 2021	Federated LearningMixture-of-Experts	CodeCode Available
UFPS: A unified framework for partially-annotated federated segmentation in heterogeneous data distribution	Nov 16, 2023	Federated LearningSegmentation	CodeCode Available
Instance-wise Batch Label Restoration via Gradients in Federated Learning	Feb 2, 2023	Federated Learning	CodeCode Available
Unveiling Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated Learning	Feb 12, 2024	Decision MakingDrift Detection	CodeCode Available
Task Diversity in Bayesian Federated Learning: Simultaneous Processing of Classification and Regression	Dec 14, 2024	Computational EfficiencyDiversity	CodeCode Available
A Joint Learning and Communications Framework for Federated Learning over Wireless Networks	Sep 17, 2019	Federated Learning	CodeCode Available
TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels	Jul 13, 2022	Federated Learning	CodeCode Available
ULDP-FL: Federated Learning with Across Silo User-Level Differential Privacy	Aug 23, 2023	Federated Learning	CodeCode Available
Intelligent Client Selection for Federated Learning using Cellular Automata	Oct 1, 2023	Federated Learning	CodeCode Available
On the Efficacy of Differentially Private Few-shot Image Classification	Feb 2, 2023	Federated LearningFew-Shot Image Classification	CodeCode Available
On the Efficiency of Privacy Attacks in Federated Learning	Apr 15, 2024	Federated Learning	CodeCode Available
Technical Insights and Legal Considerations for Advancing Federated Learning in Bioinformatics	Mar 12, 2025	Federated LearningTranslation	CodeCode Available
A Federated Approach to Predicting Emojis in Hindi Tweets	Nov 11, 2022	Federated Learning	CodeCode Available
Towards Understanding Quality Challenges of the Federated Learning for Neural Networks: A First Look from the Lens of Robustness	Jan 5, 2022	Data PoisoningFederated Learning	CodeCode Available

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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