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 3476–3500 of 6771 papers

Title	Date	Tasks	Status
Federated Reinforcement Learning for Resource Allocation in V2X Networks	Oct 15, 2023	Federated Learningreinforcement-learning	—Unverified
Federated Multi-Objective Learning	Oct 15, 2023	Federated LearningMulti-Task Learning	—Unverified
FLrce: Resource-Efficient Federated Learning with Early-Stopping Strategy	Oct 15, 2023	Deep LearningFederated Learning	CodeCode Available
A Blockchain-empowered Multi-Aggregator Federated Learning Architecture in Edge Computing with Deep Reinforcement Learning Optimization	Oct 14, 2023	Deep Reinforcement LearningEdge-computing	—Unverified
Multimodal Federated Learning in Healthcare: a Review	Oct 14, 2023	Federated Learning	—Unverified
PAGE: Equilibrate Personalization and Generalization in Federated Learning	Oct 13, 2023	Federated Learning	—Unverified
PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning	Oct 13, 2023	Federated Learning	—Unverified
Federated Class-Incremental Learning with Prompting	Oct 13, 2023	class-incremental learningClass Incremental Learning	—Unverified
Federated Meta-Learning for Few-Shot Fault Diagnosis with Representation Encoding	Oct 13, 2023	DiagnosticFault Diagnosis	—Unverified
Price of Stability in Quality-Aware Federated Learning	Oct 13, 2023	DenoisingFederated Learning	—Unverified
Tackling Heterogeneity in Medical Federated learning via Vision Transformers	Oct 13, 2023	Federated Learning	—Unverified
A Carbon Tracking Model for Federated Learning: Impact of Quantization and Sparsification	Oct 12, 2023	Federated LearningQuantization	—Unverified
Achieving Linear Speedup with ProxSkip in Distributed Stochastic Optimization	Oct 12, 2023	Distributed OptimizationFederated Learning	—Unverified
Fed-Safe: Securing Federated Learning in Healthcare Against Adversarial Attacks	Oct 12, 2023	Federated LearningMedical Image Analysis	—Unverified
Sentinel: An Aggregation Function to Secure Decentralized Federated Learning	Oct 12, 2023	Federated LearningManagement	—Unverified
2SFGL: A Simple And Robust Protocol For Graph-Based Fraud Detection	Oct 12, 2023	Federated LearningFraud Detection	—Unverified
Histopathological Image Classification and Vulnerability Analysis using Federated Learning	Oct 11, 2023	ClassificationData Poisoning	—Unverified
FedSym: Unleashing the Power of Entropy for Benchmarking the Algorithms for Federated Learning	Oct 11, 2023	BenchmarkingDiversity	—Unverified
RaftFed: A Lightweight Federated Learning Framework for Vehicular Crowd Intelligence	Oct 11, 2023	Federated LearningPrivacy Preserving	—Unverified
Advocating for the Silent: Enhancing Federated Generalization for Non-Participating Clients	Oct 11, 2023	Federated LearningGeneralization Bounds	—Unverified
The Implications of Decentralization in Blockchained Federated Learning: Evaluating the Impact of Model Staleness and Inconsistencies	Oct 11, 2023	Federated Learning	—Unverified
Asynchronous Federated Learning with Incentive Mechanism Based on Contract Theory	Oct 10, 2023	Federated Learning	—Unverified
HoloFed: Environment-Adaptive Positioning via Multi-band Reconfigurable Holographic Surfaces and Federated Learning	Oct 10, 2023	Federated LearningPosition	—Unverified
Federated Quantum Machine Learning with Differential Privacy	Oct 10, 2023	Binary ClassificationFederated Learning	—Unverified
Exploring adversarial attacks in federated learning for medical imaging	Oct 10, 2023	Federated LearningMedical Image Analysis	—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