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 4251–4275 of 6771 papers

Title	Date	Tasks	Status	Hype
Blockchain-based Monitoring for Poison Attack Detection in Decentralized Federated Learning	Sep 30, 2022	Federated Learning	—Unverified	0
FedTrees: A Novel Computation-Communication Efficient Federated Learning Framework Investigated in Smart Grids	Sep 30, 2022	energy managementEnsemble Learning	—Unverified	0
Kernel Normalized Convolutional Networks for Privacy-Preserving Machine Learning	Sep 30, 2022	Federated LearningPrivacy Preserving	—Unverified	0
Federated Training of Dual Encoding Models on Small Non-IID Client Datasets	Sep 30, 2022	Federated LearningRepresentation Learning	—Unverified	0
Vertical Semi-Federated Learning for Efficient Online Advertising	Sep 30, 2022	Federated LearningVertical Federated Learning	—Unverified	0
Sparse Random Networks for Communication-Efficient Federated Learning	Sep 30, 2022	Federated Learning	CodeCode Available	1
Rethinking Data Heterogeneity in Federated Learning: Introducing a New Notion and Standard Benchmarks	Sep 30, 2022	Federated Learning	CodeCode Available	1
Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction	Sep 30, 2022	Federated LearningPrivacy Preserving	—Unverified	0
PrivMVMF: Privacy-Preserving Multi-View Matrix Factorization for Recommender Systems	Sep 29, 2022	Federated LearningPrivacy Preserving	—Unverified	0
Label driven Knowledge Distillation for Federated Learning with non-IID Data	Sep 29, 2022	Federated LearningKnowledge Distillation	—Unverified	0
Meta Knowledge Condensation for Federated Learning	Sep 29, 2022	Federated Learning	—Unverified	0
A Secure Federated Learning Framework for Residential Short Term Load Forecasting	Sep 29, 2022	Demand ForecastingFederated Learning	—Unverified	0
Federated Stain Normalization for Computational Pathology	Sep 29, 2022	DiversityFederated Learning	CodeCode Available	0
Joint Optimization of Energy Consumption and Completion Time in Federated Learning	Sep 29, 2022	CPUFederated Learning	—Unverified	0
Momentum Gradient Descent Federated Learning with Local Differential Privacy	Sep 28, 2022	Federated LearningPrivacy Preserving	—Unverified	0
FedVeca: Federated Vectorized Averaging on Non-IID Data with Adaptive Bi-directional Global Objective	Sep 28, 2022	Federated Learning	—Unverified	0
Securing Federated Learning against Overwhelming Collusive Attackers	Sep 28, 2022	Federated LearningPrivacy Preserving	—Unverified	0
A Snapshot of the Frontiers of Client Selection in Federated Learning	Sep 27, 2022	Federated LearningPrivacy Preserving	—Unverified	0
FAIR-FATE: Fair Federated Learning with Momentum	Sep 27, 2022	FairnessFederated Learning	CodeCode Available	0
Semi-Synchronous Personalized Federated Learning over Mobile Edge Networks	Sep 27, 2022	Federated LearningPersonalized Federated Learning	—Unverified	0
FedStack: Personalized activity monitoring using stacked federated learning	Sep 27, 2022	Federated Learning	—Unverified	0
FaRO 2: an Open Source, Configurable Smart City Framework for Real-Time Distributed Vision and Biometric Systems	Sep 26, 2022	Federated LearningSelf-Driving Cars	—Unverified	0
An Energy Optimized Specializing DAG Federated Learning based on Event Triggered Communication	Sep 26, 2022	Federated Learning	—Unverified	0
Knowledge-aided Federated Learning for Energy-limited Wireless Networks	Sep 25, 2022	Federated LearningScheduling	—Unverified	0
On the Stability Analysis of Open Federated Learning Systems	Sep 25, 2022	Federated Learning	—Unverified	0

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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