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 3926–3950 of 6771 papers

Title	Date	Tasks	Status
RHFedMTL: Resource-Aware Hierarchical Federated Multi-Task Learning	Jun 1, 2023	Federated LearningMulti-Task Learning	—Unverified
Cooperative Hardware-Prompt Learning for Snapshot Compressive Imaging	Jun 1, 2023	Federated LearningPrompt Learning	—Unverified
Towards Bias Correction of FedAvg over Nonuniform and Time-Varying Communications	Jun 1, 2023	Federated Learning	—Unverified
Learning Across Decentralized Multi-Modal Remote Sensing Archives with Federated Learning	Jun 1, 2023	Federated Learningimage-classification	—Unverified
Byzantine-Robust Clustered Federated Learning	Jun 1, 2023	ClusteringFederated Learning	CodeCode Available
CRS-FL: Conditional Random Sampling for Communication-Efficient and Privacy-Preserving Federated Learning	Jun 1, 2023	Federated LearningPrivacy Preserving	—Unverified
Federated Learning in the Presence of Adversarial Client Unavailability	May 31, 2023	Federated LearningSelection bias	—Unverified
FedCSD: A Federated Learning Based Approach for Code-Smell Detection	May 31, 2023	Federated Learning	—Unverified
Adaptive Self-Distillation for Minimizing Client Drift in Heterogeneous Federated Learning	May 31, 2023	Federated Learning	—Unverified
An Empirical Study of Federated Learning on IoT-Edge Devices: Resource Allocation and Heterogeneity	May 31, 2023	Federated Learning	—Unverified
A Federated Channel Modeling System using Generative Neural Networks	May 30, 2023	Federated LearningGenerative Adversarial Network	—Unverified
Reducing Communication for Split Learning by Randomized Top-k Sparsification	May 29, 2023	Federated LearningQuantization	—Unverified
Global Layers: Non-IID Tabular Federated Learning	May 29, 2023	Federated Learning	CodeCode Available
Deep Equilibrium Models Meet Federated Learning	May 29, 2023	Federated Learning	—Unverified
Partially Personalized Federated Learning: Breaking the Curse of Data Heterogeneity	May 29, 2023	Federated LearningPersonalized Federated Learning	—Unverified
A Meta-learning Framework for Tuning Parameters of Protection Mechanisms in Trustworthy Federated Learning	May 28, 2023	Federated LearningMeta-Learning	—Unverified
Federated Empirical Risk Minimization via Second-Order Method	May 27, 2023	Federated Learningquantile regression	—Unverified
Channel and Gradient-Importance Aware Device Scheduling for Over-the-Air Federated Learning	May 26, 2023	Federated LearningPrivacy Preserving	—Unverified
Federated Learning for Semantic Parsing: Task Formulation, Evaluation Setup, New Algorithms	May 26, 2023	Federated LearningSemantic Parsing	CodeCode Available
A Framework for Incentivized Collaborative Learning	May 26, 2023	Federated Learning	—Unverified
Parameter-Efficient Fine-Tuning without Introducing New Latency	May 26, 2023	Federated Learningparameter-efficient fine-tuning	CodeCode Available
Secure Vertical Federated Learning Under Unreliable Connectivity	May 26, 2023	Federated LearningPrivacy Preserving	—Unverified
Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devices	May 26, 2023	Federated Learning	—Unverified
FAVANO: Federated AVeraging with Asynchronous NOdes	May 25, 2023	Federated Learning	—Unverified
Distributed Trust Through the Lens of Software Architecture	May 25, 2023	AttributeFederated Learning	—Unverified

Show:10 25 50

← PrevPage 158 of 271Next →

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