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 551–575 of 6771 papers

Title	Date	Tasks	Status	Hype
FedLess: Secure and Scalable Federated Learning Using Serverless Computing	Nov 5, 2021	Federated LearningManagement	CodeCode Available	1
Parameterized Knowledge Transfer for Personalized Federated Learning	Nov 4, 2021	Federated LearningPersonalized Federated Learning	CodeCode Available	1
FedFly: Towards Migration in Edge-based Distributed Federated Learning	Nov 2, 2021	Federated LearningPrivacy Preserving	CodeCode Available	1
FedSim: Similarity guided model aggregation for Federated Learning	Nov 2, 2021	Federated Learningmodel	CodeCode Available	1
Implicit Model Specialization through DAG-based Decentralized Federated Learning	Nov 1, 2021	Federated Learning	CodeCode Available	1
Resource-Efficient Federated Learning	Nov 1, 2021	FairnessFederated Learning	CodeCode Available	1
Efficient passive membership inference attack in federated learning	Oct 31, 2021	Federated LearningInference Attack	CodeCode Available	1
Improving Fairness via Federated Learning	Oct 29, 2021	FairnessFederated Learning	CodeCode Available	1
Gradient Inversion with Generative Image Prior	Oct 28, 2021	Federated Learning	CodeCode Available	1
CAFE: Catastrophic Data Leakage in Vertical Federated Learning	Oct 26, 2021	Federated LearningVertical Federated Learning	CodeCode Available	1
Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee	Oct 26, 2021	Decision MakingFederated Learning	CodeCode Available	1
Qu-ANTI-zation: Exploiting Quantization Artifacts for Achieving Adversarial Outcomes	Oct 26, 2021	Backdoor AttackFederated Learning	CodeCode Available	1
FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective	Oct 26, 2021	Federated LearningModel Poisoning	CodeCode Available	1
Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models	Oct 25, 2021	Federated Learning	CodeCode Available	1
DistFL: Distribution-aware Federated Learning for Mobile Scenarios	Oct 22, 2021	Federated LearningPrivacy Preserving	CodeCode Available	1
A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and Comparison	Oct 19, 2021	Activity RecognitionFederated Learning	CodeCode Available	1
Towards Federated Bayesian Network Structure Learning with Continuous Optimization	Oct 18, 2021	Federated Learning	CodeCode Available	1
Adapt to Adaptation: Learning Personalization for Cross-Silo Federated Learning	Oct 15, 2021	Federated Learning	CodeCode Available	1
Federated Learning for COVID-19 Detection with Generative Adversarial Networks in Edge Cloud Computing	Oct 14, 2021	Cloud ComputingFederated Learning	CodeCode Available	1
Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated Learning	Oct 13, 2021	Adversarial AttackFederated Learning	CodeCode Available	1
Deep Federated Learning for Autonomous Driving	Oct 12, 2021	Autonomous DrivingFederated Learning	CodeCode Available	1
ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training	Oct 11, 2021	Federated LearningImage Segmentation	CodeCode Available	1
Federated Learning from Small Datasets	Oct 7, 2021	Federated Learning	CodeCode Available	1
Communication-Efficient Federated Learning with Binary Neural Networks	Oct 5, 2021	BinarizationFederated Learning	CodeCode Available	1
DESTRESS: Computation-Optimal and Communication-Efficient Decentralized Nonconvex Finite-Sum Optimization	Oct 4, 2021	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