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 1176–1200 of 6771 papers

Title	Date	Tasks	Status	Score
Federated Learning with Reduced Information Leakage and Computation	Oct 10, 2023	Federated LearningPrivacy Preserving	CodeCode Available	5
Aggregation Delayed Federated Learning	Aug 17, 2021	BIG-bench Machine LearningFederated Learning	CodeCode Available	5
AugFL: Augmenting Federated Learning with Pretrained Models	Mar 4, 2025	Federated LearningMeta-Learning	CodeCode Available	5
Federated Learning with Reservoir State Analysis for Time Series Anomaly Detection	Feb 8, 2025	Anomaly DetectionComputational Efficiency	CodeCode Available	5
Federated Learning with Non-IID Data	Jun 2, 2018	Federated Learning	CodeCode Available	5
Federated Learning with Only Positive Labels	Apr 21, 2020	Federated LearningMulti-class Classification	CodeCode Available	5
Aggregating Intrinsic Information to Enhance BCI Performance through Federated Learning	Aug 14, 2023	Brain Computer InterfaceEEG	CodeCode Available	5
Labeling Chaos to Learning Harmony: Federated Learning with Noisy Labels	Aug 19, 2022	Federated LearningLearning with noisy labels	CodeCode Available	5
Federated Learning with Intermediate Representation Regularization	Oct 28, 2022	Federated Learning	CodeCode Available	5
Federated Learning With Individualized Privacy Through Client Sampling	Jan 29, 2025	Federated Learning	CodeCode Available	5
Federated Learning with Heterogeneous Data Handling for Robust Vehicular Object Detection	May 2, 2024	Autonomous DrivingFederated Learning	CodeCode Available	5
Federated Learning for Time-Series Healthcare Sensing with Incomplete Modalities	May 20, 2024	Computational EfficiencyFederated Learning	CodeCode Available	5
Attribute Inference Attacks for Federated Regression Tasks	Nov 19, 2024	AttributeFederated Learning	CodeCode Available	5
Attentive Federated Learning for Concept Drift in Distributed 5G Edge Networks	Nov 14, 2021	Continual LearningEdge-computing	CodeCode Available	5
Attention on Personalized Clinical Decision Support System: Federated Learning Approach	Jan 22, 2024	Disease PredictionFederated Learning	CodeCode Available	5
Attacks on fairness in Federated Learning	Nov 21, 2023	AttributeBackdoor Attack	CodeCode Available	5
Federated Learning with Additional Mechanisms on Clients to Reduce Communication Costs	Aug 16, 2019	Federated Learning	CodeCode Available	5
Attack-Resistant Federated Learning with Residual-based Reweighting	Dec 24, 2019	Federated Learningregression	CodeCode Available	5
Federated Learning with a Single Shared Image	Jun 18, 2024	Federated LearningKnowledge Distillation	CodeCode Available	5
Federated Learning via Plurality Vote	Oct 6, 2021	Federated LearningQuantization	CodeCode Available	5
ATR-Bench: A Federated Learning Benchmark for Adaptation, Trust, and Reasoning	May 22, 2025	Federated Learning	CodeCode Available	5
A generic framework for privacy preserving deep learning	Nov 9, 2018	Deep LearningFederated Learning	CodeCode Available	5
Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysis	Oct 30, 2024	Federated Learning	CodeCode Available	5
Federated Learning under Partially Class-Disjoint Data via Manifold Reshaping	May 29, 2024	Federated Learning	CodeCode Available	5
Federated Learning via Consensus Mechanism on Heterogeneous Data: A New Perspective on Convergence	Nov 21, 2023	FairnessFederated Learning	CodeCode Available	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