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 5626–5650 of 6771 papers

Title	Date	Tasks	Status
LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learning	Sep 29, 2021	Federated Learning	—Unverified
Hybrid Local SGD for Federated Learning with Heterogeneous Communications	Sep 29, 2021	Federated Learning	—Unverified
FSL: Federated Supermask Learning	Sep 29, 2021	Federated Learning	—Unverified
FLBoost: On-the-Fly Fine-tuning Boosts Federated Learning via Data-free Distillation	Sep 29, 2021	Federated Learning	—Unverified
FEVERLESS: Fast and Secure Vertical Federated Learning based on XGBoost for Decentralized Labels	Sep 29, 2021	Federated LearningVertical Federated Learning	—Unverified
FedNAS: Federated Deep Learning via Neural Architecture Search	Sep 29, 2021	Deep LearningFederated Learning	—Unverified
FedMorph: Communication Efficient Federated Learning via Morphing Neural Network	Sep 29, 2021	Federated LearningMORPH	—Unverified
On Heterogeneously Distributed Data, Sparsity Matters	Sep 29, 2021	Federated LearningPersonalized Federated Learning	—Unverified
Towards Communication-Efficient and Privacy-Preserving Federated Representation Learning	Sep 29, 2021	Contrastive LearningFederated Learning	—Unverified
Federated Learning with Data-Agnostic Distribution Fusion	Sep 29, 2021	Federated Learning	—Unverified
Personalized Heterogeneous Federated Learning with Gradient Similarity	Sep 29, 2021	Federated Learning	—Unverified
Personalized Neural Architecture Search for Federated Learning	Sep 29, 2021	Federated LearningNeural Architecture Search	—Unverified
FEDERATED LEARNING FRAMEWORK BASED ON TRIMMED MEAN AGGREGATION RULES	Sep 29, 2021	Federated Learning	—Unverified
Picking Daisies in Private: Federated Learning from Small Datasets	Sep 29, 2021	Federated Learning	—Unverified
Positive and Unlabeled Federated Learning	Sep 29, 2021	Federated Learning	—Unverified
Practical and Private Heterogeneous Federated Learning	Sep 29, 2021	Federated LearningPrivacy Preserving	—Unverified
Predictive Maintenance for Optical Networks in Robust Collaborative Learning	Sep 29, 2021	Federated Learning	—Unverified
Federated Inference through Aligning Local Representations and Learning a Consensus Graph	Sep 29, 2021	Federated Learning	—Unverified
Federated Contrastive Representation Learning with Feature Fusion and Neighborhood Matching	Sep 29, 2021	Contrastive LearningFederated Learning	—Unverified
FedDrop: Trajectory-weighted Dropout for Efficient Federated Learning	Sep 29, 2021	Federated Learning	—Unverified
FedDiscrete: A Secure Federated Learning Algorithm Against Weight Poisoning	Sep 29, 2021	Federated Learning	—Unverified
Provable Federated Adversarial Learning via Min-max Optimization	Sep 29, 2021	Federated Learning	—Unverified
FedBABU: Toward Enhanced Representation for Federated Image Classification	Sep 29, 2021	ClassificationFederated Learning	—Unverified
Fairness-aware Federated Learning	Sep 29, 2021	FairnessFederated Learning	—Unverified
EP-GAN: Unsupervised Federated Learning with Expectation-Propagation Prior GAN	Sep 29, 2021	Federated Learningspeech-recognition	—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