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 5951–5975 of 6771 papers

Title	Date	Tasks	Status	Hype
Multi-Tier Federated Learning for Vertically Partitioned Data	Feb 6, 2021	Federated Learning	—Unverified	0
Estimation of Microphone Clusters in Acoustic Sensor Networks using Unsupervised Federated Learning	Feb 5, 2021	ClusteringFederated Learning	CodeCode Available	0
Federated Reconstruction: Partially Local Federated Learning	Feb 5, 2021	Collaborative FilteringFederated Learning	CodeCode Available	1
Federated Learning on the Road: Autonomous Controller Design for Connected and Autonomous Vehicles	Feb 5, 2021	Autonomous VehiclesFederated Learning	—Unverified	0
Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning	Feb 5, 2021	Distributed OptimizationFederated Learning	—Unverified	0
Generalization Bounds for Noisy Iterative Algorithms Using Properties of Additive Noise Channels	Feb 5, 2021	Federated LearningGeneralization Bounds	—Unverified	0
DEAL: Decremental Energy-Aware Learning in a Federated System	Feb 5, 2021	energy managementFederated Learning	—Unverified	0
SAFELearning: Enable Backdoor Detectability In Federated Learning With Secure Aggregation	Feb 4, 2021	Anomaly DetectionFederated Learning	—Unverified	0
Semi-Synchronous Federated Learning for Energy-Efficient Training and Accelerated Convergence in Cross-Silo Settings	Feb 4, 2021	BIG-bench Machine LearningFederated Learning	—Unverified	0
FedAUX: Leveraging Unlabeled Auxiliary Data in Federated Learning	Feb 4, 2021	Federated LearningUnsupervised Pre-training	CodeCode Available	1
A Bayesian Federated Learning Framework with Online Laplace Approximation	Feb 3, 2021	Federated Learning	—Unverified	0
Federated Learning on Non-IID Data Silos: An Experimental Study	Feb 3, 2021	BIG-bench Machine LearningFederated Learning	CodeCode Available	1
Provably Secure Federated Learning against Malicious Clients	Feb 3, 2021	Activity RecognitionFederated Learning	—Unverified	0
FedProf: Selective Federated Learning with Representation Profiling	Feb 2, 2021	Federated LearningPrivacy Preserving	—Unverified	0
FEDZIP: A Compression Framework for Communication-Efficient Federated Learning	Feb 2, 2021	BIG-bench Machine LearningClustering	CodeCode Available	0
Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challenges	Feb 2, 2021	Federated Learning	—Unverified	0
Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning	Feb 1, 2021	Federated Learning	—Unverified	0
Scaling Federated Learning for Fine-tuning of Large Language Models	Feb 1, 2021	Federated LearningSentiment Analysis	—Unverified	0
Decentralized Federated Learning Preserves Model and Data Privacy	Feb 1, 2021	Anomaly DetectionFederated Learning	—Unverified	0
A_Blockchain-Based_Decentralized_Federated_Learning_Framework_with_Committee_Consensus	Jan 31, 2021	Federated Learning	—Unverified	0
On Data Efficiency of Meta-learning	Jan 30, 2021	Federated LearningMeta-Learning	—Unverified	0
Federated Learning over Wireless Device-to-Device Networks: Algorithms and Convergence Analysis	Jan 29, 2021	Cloud ComputingDimensionality Reduction	—Unverified	0
Edge Federated Learning Via Unit-Modulus Over-The-Air Computation	Jan 28, 2021	Autonomous DrivingFederated Learning	CodeCode Available	1
Self-supervised Cross-silo Federated Neural Architecture Search	Jan 28, 2021	Federated LearningNeural Architecture Search	—Unverified	0
Differential Privacy Meets Federated Learning under Communication Constraints	Jan 28, 2021	Federated LearningModel Compression	—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