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 4601–4625 of 6771 papers

Title	Date	Tasks	Status
Federated Graph Representation Learning using Self-Supervision	Oct 27, 2022	Federated LearningGraph Representation Learning	—Unverified
Addressing Heterogeneity in Federated Learning via Distributional Transformation	Oct 26, 2022	Data AugmentationFederated Learning	CodeCode Available
Personalized Federated Learning via Heterogeneous Modular Networks	Oct 26, 2022	Federated LearningPersonalized Federated Learning	CodeCode Available
FeDXL: Provable Federated Learning for Deep X-Risk Optimization	Oct 26, 2022	Federated Learning	CodeCode Available
Coresets for Vertical Federated Learning: Regularized Linear Regression and K-Means Clustering	Oct 26, 2022	ClusteringFederated Learning	CodeCode Available
Efficient Vertical Federated Learning Method for Ridge Regression of Large-Scale Samples via Least-Squares Solution	Oct 26, 2022	Data IntegrationFederated Learning	CodeCode Available
Hierarchical Federated Learning with Momentum Acceleration in Multi-Tier Networks	Oct 26, 2022	Federated Learning	—Unverified
FedGRec: Federated Graph Recommender System with Lazy Update of Latent Embeddings	Oct 25, 2022	Federated LearningRecommendation Systems	—Unverified
Federated Learning Using Variance Reduced Stochastic Gradient for Probabilistically Activated Agents	Oct 25, 2022	Federated LearningStochastic Optimization	—Unverified
UTILIZING FEDERATED LEARNING AND META LEARNING FOR FEW-SHOT LEARNING ON EDGE DEVICES	Oct 25, 2022	Federated LearningFew-Shot Learning	—Unverified
Subspace Recovery from Heterogeneous Data with Non-isotropic Noise	Oct 24, 2022	Federated Learning	—Unverified
Investigating Neuron Disturbing in Fusing Heterogeneous Neural Networks	Oct 24, 2022	Federated Learningvalid	—Unverified
Federated Learning and Meta Learning: Approaches, Applications, and Directions	Oct 24, 2022	Decision MakingFederated Learning	—Unverified
Detection and Prevention Against Poisoning Attacks in Federated Learning	Oct 24, 2022	Federated Learning	—Unverified
Provably Doubly Accelerated Federated Learning: The First Theoretically Successful Combination of Local Training and Communication Compression	Oct 24, 2022	Distributed OptimizationFederated Learning	—Unverified
On-Demand Sampling: Learning Optimally from Multiple Distributions	Oct 22, 2022	FairnessFederated Learning	CodeCode Available
Mixed Precision Quantization to Tackle Gradient Leakage Attacks in Federated Learning	Oct 22, 2022	Federated LearningQuantization	—Unverified
Towards Quantum-Enabled 6G Slicing	Oct 21, 2022	Deep Reinforcement LearningFederated Learning	—Unverified
FedRecover: Recovering from Poisoning Attacks in Federated Learning using Historical Information	Oct 20, 2022	Federated Learning	—Unverified
Federated Learning via Unmanned Aerial Vehicle	Oct 20, 2022	Federated LearningScheduling	—Unverified
Analyzing the Robustness of Decentralized Horizontal and Vertical Federated Learning Architectures in a Non-IID Scenario	Oct 20, 2022	Data PoisoningFederated Learning	—Unverified
An Improved Algorithm for Clustered Federated Learning	Oct 20, 2022	ClusteringFederated Learning	CodeCode Available
Backdoor Attack and Defense in Federated Generative Adversarial Network-based Medical Image Synthesis	Oct 19, 2022	Backdoor AttackData Augmentation	—Unverified
Latency Aware Semi-synchronous Client Selection and Model Aggregation for Wireless Federated Learning	Oct 19, 2022	Federated Learning	—Unverified
Channel-driven Decentralized Bayesian Federated Learning for Trustworthy Decision Making in D2D Networks	Oct 19, 2022	Decision MakingFederated Learning	—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