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 451–475 of 6771 papers

Title	Date	Tasks	Status	Hype
DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training	Jun 1, 2022	Federated LearningPersonalized Federated Learning	CodeCode Available	1
Can Foundation Models Help Us Achieve Perfect Secrecy?	May 27, 2022	Federated LearningIn-Context Learning	CodeCode Available	1
FedFormer: Contextual Federation with Attention in Reinforcement Learning	May 27, 2022	Federated Learningreinforcement-learning	CodeCode Available	1
FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction	May 26, 2022	Domain GeneralizationFederated Learning	CodeCode Available	1
Masked Jigsaw Puzzle: A Versatile Position Embedding for Vision Transformers	May 25, 2022	Federated LearningPosition	CodeCode Available	1
Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy Guarantees	May 24, 2022	Federated Learning	CodeCode Available	1
Towards a Defense Against Federated Backdoor Attacks Under Continuous Training	May 24, 2022	Continual LearningFederated Learning	CodeCode Available	1
Optimizing Performance of Federated Person Re-identification: Benchmarking and Analysis	May 24, 2022	BenchmarkingFederated Learning	CodeCode Available	1
Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering	May 23, 2022	ClusteringFederated Learning	CodeCode Available	1
Test-Time Robust Personalization for Federated Learning	May 22, 2022	Federated Learning	CodeCode Available	1
FedAdapter: Efficient Federated Learning for Modern NLP	May 20, 2022	Federated Learning	CodeCode Available	1
FedILC: Weighted Geometric Mean and Invariant Gradient Covariance for Federated Learning on Non-IID Data	May 19, 2022	Federated Learning	CodeCode Available	1
Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging	May 17, 2022	Federated LearningMedical Image Analysis	CodeCode Available	1
Recovering Private Text in Federated Learning of Language Models	May 17, 2022	Federated LearningWord Embeddings	CodeCode Available	1
Federated Learning Under Intermittent Client Availability and Time-Varying Communication Constraints	May 13, 2022	BenchmarkingFederated Learning	CodeCode Available	1
ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learning	May 9, 2022	Federated Learning	CodeCode Available	1
Communication-Efficient Adaptive Federated Learning	May 5, 2022	Federated LearningQuantization	CodeCode Available	1
FedMix: Mixed Supervised Federated Learning for Medical Image Segmentation	May 4, 2022	Federated LearningImage Segmentation	CodeCode Available	1
FedGiA: An Efficient Hybrid Algorithm for Federated Learning	May 3, 2022	Federated Learning	CodeCode Available	1
FEDIC: Federated Learning on Non-IID and Long-Tailed Data via Calibrated Distillation	Apr 30, 2022	Federated LearningLong-tail Learning	CodeCode Available	1
Federated Learning on Heterogeneous and Long-Tailed Data via Classifier Re-Training with Federated Features	Apr 28, 2022	Federated LearningPrivacy Preserving	CodeCode Available	1
Poisoning Deep Learning Based Recommender Model in Federated Learning Scenarios	Apr 26, 2022	Deep LearningFederated Learning	CodeCode Available	1
Federated Learning Enables Big Data for Rare Cancer Boundary Detection	Apr 22, 2022	Boundary DetectionFederated Learning	CodeCode Available	1
Federated Learning via Inexact ADMM	Apr 22, 2022	Federated Learning	CodeCode Available	1
FedKL: Tackling Data Heterogeneity in Federated Reinforcement Learning by Penalizing KL Divergence	Apr 18, 2022	Federated Learningreinforcement-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