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 1451–1475 of 6771 papers

Title	Date	Tasks	Status	Score
A V2X-based Privacy Preserving Federated Measuring and Learning System	Jan 24, 2024	Autonomous VehiclesDecision Making	CodeCode Available	5
FSL-SAGE: Accelerating Federated Split Learning via Smashed Activation Gradient Estimation	May 29, 2025	Federated Learning	CodeCode Available	5
Fair Resource Allocation in Federated Learning	May 25, 2019	FairnessFederated Learning	CodeCode Available	5
FUNAvg: Federated Uncertainty Weighted Averaging for Datasets with Diverse Labels	Jul 10, 2024	Federated LearningPrivacy Preserving	CodeCode Available	5
FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clustering	Jan 29, 2023	ClusteringFederated Learning	CodeCode Available	5
Fed-CO2: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated Learning	Dec 21, 2023	Domain GeneralizationFederated Learning	CodeCode Available	5
FDA-Opt: Communication-Efficient Federated Fine-Tuning of Language Models	May 7, 2025	Federated Learning	CodeCode Available	5
Generalizable Learning Reconstruction for Accelerating MR Imaging via Federated Neural Architecture Search	Aug 27, 2023	Efficient Neural NetworkFairness	CodeCode Available	5
FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning	Nov 16, 2021	Federated LearningPrivacy Preserving	CodeCode Available	5
Decentralized Personalized Federated Learning based on a Conditional Sparse-to-Sparser Scheme	Apr 24, 2024	Federated LearningPersonalized Federated Learning	CodeCode Available	5
Generative Gradient Inversion via Over-Parameterized Networks in Federated Learning	Jan 1, 2023	Federated Learning	CodeCode Available	5
FedCore: Straggler-Free Federated Learning with Distributed Coresets	Jan 31, 2024	Federated Learning	CodeCode Available	5
FedDebug: Systematic Debugging for Federated Learning Applications	Jan 9, 2023	Fault localizationFederated Learning	CodeCode Available	5
Policy-Based Federated Learning	Mar 14, 2020	Federated Learningimage-classification	CodeCode Available	5
Federated Neural Radiance Fields	May 2, 2023	Federated LearningNeRF	CodeCode Available	5
FedCAP: Robust Federated Learning via Customized Aggregation and Personalization	Oct 16, 2024	Anomaly DetectionFederated Learning	CodeCode Available	5
Decentralized Sporadic Federated Learning: A Unified Algorithmic Framework with Convergence Guarantees	Feb 5, 2024	Federated Learning	CodeCode Available	5
Bidirectional compression in heterogeneous settings for distributed or federated learning with partial participation: tight convergence guarantees	Jun 25, 2020	Federated Learning	CodeCode Available	5
Adaptive Federated Learning with Auto-Tuned Clients	Jun 19, 2023	Federated Learning	CodeCode Available	5
FedCAR: Cross-client Adaptive Re-weighting for Generative Models in Federated Learning	Dec 16, 2024	Federated LearningImage Generation	CodeCode Available	5
Communication-Efficient Federated Linear and Deep Generalized Canonical Correlation Analysis	Sep 25, 2021	Distributed ComputingDistributed Optimization	CodeCode Available	5
A Federated Data-Driven Evolutionary Algorithm for Expensive Multi/Many-objective Optimization	Jun 22, 2021	Evolutionary AlgorithmsFederated Learning	CodeCode Available	5
ARIA: On the Interaction Between Architectures, Initialization and Aggregation Methods for Federated Visual Classification	Nov 24, 2023	Federated Learningimage-classification	CodeCode Available	5
FedBug: A Bottom-Up Gradual Unfreezing Framework for Federated Learning	Jul 19, 2023	Federated Learning	CodeCode Available	5
FedBrain-Distill: Communication-Efficient Federated Brain Tumor Classification Using Ensemble Knowledge Distillation on Non-IID Data	Sep 9, 2024	Brain Tumor ClassificationFederated 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