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 701–725 of 6771 papers

Title	Date	Tasks	Status	Hype
Ditto: Fair and Robust Federated Learning Through Personalization	Dec 8, 2020	FairnessFederated Learning	CodeCode Available	1
Unleashing the Tiger: Inference Attacks on Split Learning	Dec 4, 2020	Federated Learning	CodeCode Available	1
Robust Federated Learning with Noisy Labels	Dec 3, 2020	Federated LearningLearning with noisy labels	CodeCode Available	1
Inverting Gradients - How easy is it to break privacy in federated learning?	Dec 1, 2020	Federated Learning	CodeCode Available	1
Toward Multiple Federated Learning Services Resource Sharing in Mobile Edge Networks	Nov 25, 2020	CPUEdge-computing	CodeCode Available	1
A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in Federated Learning	Nov 20, 2020	Adversarial DefenseAdversarial Robustness	CodeCode Available	1
Reconfigurable Intelligent Surface Enabled Federated Learning: A Unified Communication-Learning Design Approach	Nov 20, 2020	Federated Learning	CodeCode Available	1
Federated Composite Optimization	Nov 17, 2020	Federated Learning	CodeCode Available	1
Federated Knowledge Distillation	Nov 4, 2020	Federated LearningKnowledge Distillation	CodeCode Available	1
Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognition	Oct 26, 2020	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	CodeCode Available	1
Optimal Client Sampling for Federated Learning	Oct 26, 2020	Federated Learning	CodeCode Available	1
Throughput-Optimal Topology Design for Cross-Silo Federated Learning	Oct 23, 2020	Federated Learning	CodeCode Available	1
Feature Inference Attack on Model Predictions in Vertical Federated Learning	Oct 20, 2020	Federated LearningInference Attack	CodeCode Available	1
Federated Bayesian Optimization via Thompson Sampling	Oct 20, 2020	Bayesian OptimizationComputational Efficiency	CodeCode Available	1
R-GAP: Recursive Gradient Attack on Privacy	Oct 15, 2020	Federated Learning	CodeCode Available	1
FedGroup: Efficient Clustered Federated Learning via Decomposed Data-Driven Measure	Oct 14, 2020	ClusteringFederated Learning	CodeCode Available	1
Oort: Efficient Federated Learning via Guided Participant Selection	Oct 12, 2020	Federated Learning	CodeCode Available	1
TextHide: Tackling Data Privacy in Language Understanding Tasks	Oct 12, 2020	Federated LearningNatural Language Understanding	CodeCode Available	1
Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms	Oct 11, 2020	Federated Learning	CodeCode Available	1
Specialized federated learning using a mixture of experts	Oct 5, 2020	Federated LearningMixture-of-Experts	CodeCode Available	1
HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients	Oct 3, 2020	Federated Learning	CodeCode Available	1
Practical One-Shot Federated Learning for Cross-Silo Setting	Oct 2, 2020	Federated LearningTransfer Learning	CodeCode Available	1
Model-sharing Games: Analyzing Federated Learning Under Voluntary Participation	Oct 2, 2020	Federated Learning	CodeCode Available	1
Intrusion Detection with Segmented Federated Learning for Large-Scale Multiple LANs	Sep 28, 2020	Federated LearningIntrusion Detection	CodeCode Available	1
Over-the-Air Federated Learning from Heterogeneous Data	Sep 27, 2020	Federated Learning	CodeCode Available	1

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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