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 126–150 of 6771 papers

Title	Date	Tasks	Status	Hype	Score
Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognition	Oct 26, 2020	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	CodeCode Available	1	5
Collaborative Fairness in Federated Learning	Aug 27, 2020	Collaborative FairnessFairness	CodeCode Available	1	5
ACCO: Accumulate While You Communicate for Communication-Overlapped Sharded LLM Training	Jun 3, 2024	Distributed OptimizationFederated Learning	CodeCode Available	1	5
An Efficient and Reliable Asynchronous Federated Learning Scheme for Smart Public Transportation	Aug 15, 2022	Federated Learning	CodeCode Available	1	5
Acceleration of Federated Learning with Alleviated Forgetting in Local Training	Mar 5, 2022	Distributed OptimizationFederated Learning	CodeCode Available	1	5
Analyzing Federated Learning through an Adversarial Lens	Nov 29, 2018	Federated LearningModel Poisoning	CodeCode Available	1	5
CoCoFL: Communication- and Computation-Aware Federated Learning via Partial NN Freezing and Quantization	Mar 10, 2022	FairnessFederated Learning	CodeCode Available	1	5
Combating Exacerbated Heterogeneity for Robust Models in Federated Learning	Mar 1, 2023	Federated Learning	CodeCode Available	1	5
Client-Level Differential Privacy via Adaptive Intermediary in Federated Medical Imaging	Jul 24, 2023	Federated Learning	CodeCode Available	1	5
Classifier Clustering and Feature Alignment for Federated Learning under Distributed Concept Drift	Oct 24, 2024	ClusteringFederated Learning	CodeCode Available	1	5
Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off	Feb 10, 2024	Federated Learning	CodeCode Available	1	5
Enhancing Efficiency in Multidevice Federated Learning through Data Selection	Nov 8, 2022	Federated Learning	CodeCode Available	1	5
A Hybrid Self-Supervised Learning Framework for Vertical Federated Learning	Aug 18, 2022	Federated LearningInference Attack	CodeCode Available	1	5
Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning	Apr 25, 2023	Contrastive LearningFederated Learning	CodeCode Available	1	5
CLIP-guided Federated Learning on Heterogeneous and Long-Tailed Data	Dec 14, 2023	Contrastive LearningFederated Learning	CodeCode Available	1	5
Catastrophic Data Leakage in Vertical Federated Learning	Dec 1, 2021	Federated LearningVertical Federated Learning	CodeCode Available	1	5
Agnostic Federated Learning	Feb 1, 2019	Cloud ComputingDomain Adaptation	CodeCode Available	1	5
CEFHRI: A Communication Efficient Federated Learning Framework for Recognizing Industrial Human-Robot Interaction	Aug 29, 2023	Federated Learningimage-classification	CodeCode Available	1	5
CaPC Learning: Confidential and Private Collaborative Learning	Feb 9, 2021	FairnessFederated Learning	CodeCode Available	1	5
CAR-MFL: Cross-Modal Augmentation by Retrieval for Multimodal Federated Learning with Missing Modalities	Jul 11, 2024	Data AugmentationFederated Learning	CodeCode Available	1	5
CENSOR: Defense Against Gradient Inversion via Orthogonal Subspace Bayesian Sampling	Jan 27, 2025	Federated Learning	CodeCode Available	1	5
Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning	May 12, 2021	ClusteringFederated Learning	CodeCode Available	1	5
Communication-Efficient Adaptive Federated Learning	May 5, 2022	Federated LearningQuantization	CodeCode Available	1	5
CAFE: Catastrophic Data Leakage in Vertical Federated Learning	Oct 26, 2021	Federated LearningVertical Federated Learning	CodeCode Available	1	5
A Framework for Energy and Carbon Footprint Analysis of Distributed and Federated Edge Learning	Mar 18, 2021	Federated Learning	CodeCode Available	1	5

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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