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 5401–5425 of 6771 papers

Title	Date	Tasks	Status
Practical and Private Heterogeneous Federated Learning	Sep 29, 2021	Federated LearningPrivacy Preserving	—Unverified
Federated Contrastive Representation Learning with Feature Fusion and Neighborhood Matching	Sep 29, 2021	Contrastive LearningFederated Learning	—Unverified
Robust and Personalized Federated Learning with Spurious Features: an Adversarial Approach	Sep 29, 2021	Federated LearningPersonalized Federated Learning	—Unverified
FedDiscrete: A Secure Federated Learning Algorithm Against Weight Poisoning	Sep 29, 2021	Federated Learning	—Unverified
Adapt to Adaptation: Learning to Personalize for Cross-Silo Federated Learning	Sep 29, 2021	Federated Learning	—Unverified
Secure Byzantine-Robust Federated Learning with Dimension-free Error	Sep 29, 2021	Federated Learning	—Unverified
Hybrid Local SGD for Federated Learning with Heterogeneous Communications	Sep 29, 2021	Federated Learning	—Unverified
FedBABU: Toward Enhanced Representation for Federated Image Classification	Sep 29, 2021	ClassificationFederated Learning	—Unverified
An Agnostic Approach to Federated Learning with Class Imbalance	Sep 29, 2021	Federated Learning	—Unverified
Distributed Skellam Mechanism: a Novel Approach to Federated Learning with Differential Privacy	Sep 29, 2021	Federated LearningMath	—Unverified
Fairness-aware Federated Learning	Sep 29, 2021	FairnessFederated Learning	—Unverified
FSL: Federated Supermask Learning	Sep 29, 2021	Federated Learning	—Unverified
RVFR: Robust Vertical Federated Learning via Feature Subspace Recovery	Sep 29, 2021	Federated LearningVertical Federated Learning	—Unverified
Scalable Robust Federated Learning with Provable Security Guarantees	Sep 29, 2021	Federated Learning	—Unverified
Picking Daisies in Private: Federated Learning from Small Datasets	Sep 29, 2021	Federated Learning	—Unverified
Unsupervised Federated Learning is Possible	Sep 29, 2021	Federated Learning	—Unverified
Certified Robustness for Free in Differentially Private Federated Learning	Sep 29, 2021	Federated Learning	—Unverified
Towards Generalizable Personalized Federated Learning with Adaptive Local Adaptation	Sep 29, 2021	Federated LearningMeta-Learning	—Unverified
Coordinated Attacks Against Federated Learning: A Multi-Agent Reinforcement Learning Approach	Sep 29, 2021	Federated LearningMulti-agent Reinforcement Learning	—Unverified
FEDERATED LEARNING FRAMEWORK BASED ON TRIMMED MEAN AGGREGATION RULES	Sep 29, 2021	Federated Learning	—Unverified
FedDrop: Trajectory-weighted Dropout for Efficient Federated Learning	Sep 29, 2021	Federated Learning	—Unverified
Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions	Sep 29, 2021	Federated Learningimage-classification	—Unverified
LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learning	Sep 29, 2021	Federated Learning	—Unverified
Secure Multi-Party Computation based Privacy Preserving Data Analysis in Healthcare IoT Systems	Sep 29, 2021	Federated LearningPrivacy Preserving	—Unverified
Towards Communication-Efficient and Privacy-Preserving Federated Representation Learning	Sep 29, 2021	Contrastive LearningFederated Learning	—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