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 326–350 of 6771 papers

Title	Date	Tasks	Status	Hype	Score
Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive Collaboration	Sep 20, 2023	Federated LearningPersonalized Federated Learning	CodeCode Available	1	5
ByzFL: Research Framework for Robust Federated Learning	May 30, 2025	BenchmarkingFederated Learning	CodeCode Available	1	5
FedBEVT: Federated Learning Bird's Eye View Perception Transformer in Road Traffic Systems	Apr 4, 2023	Autonomous DrivingFederated Learning	CodeCode Available	1	5
A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime Detection	Feb 7, 2023	Federated LearningPrivacy Preserving	CodeCode Available	1	5
Anomaly-Flow: A Multi-domain Federated Generative Adversarial Network for Distributed Denial-of-Service Detection	Mar 18, 2025	Federated LearningGenerative Adversarial Network	CodeCode Available	1	5
Byzantine-Robust Decentralized Learning via ClippedGossip	Feb 3, 2022	Federated Learning	CodeCode Available	1	5
FedCD: Improving Performance in non-IID Federated Learning	Jun 17, 2020	Federated Learning	CodeCode Available	1	5
Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy Guarantees	May 24, 2022	Federated Learning	CodeCode Available	1	5
Omnidirectional Transfer for Quasilinear Lifelong Learning	Apr 27, 2020	Federated LearningLifelong learning	CodeCode Available	1	5
CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning	Oct 6, 2022	Federated Learning	CodeCode Available	1	5
CAFE: Catastrophic Data Leakage in Vertical Federated Learning	Oct 26, 2021	Federated LearningVertical Federated Learning	CodeCode Available	1	5
Can Foundation Models Help Us Achieve Perfect Secrecy?	May 27, 2022	Federated LearningIn-Context Learning	CodeCode Available	1	5
Dopamine: Differentially Private Federated Learning on Medical Data	Jan 27, 2021	Federated Learning	CodeCode Available	1	5
CaPC Learning: Confidential and Private Collaborative Learning	Feb 9, 2021	FairnessFederated Learning	CodeCode Available	1	5
Can Textual Gradient Work in Federated Learning?	Feb 27, 2025	Federated 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
FedCMR: Federated Cross-Modal Retrieval	Jul 1, 2021	Cross-Modal RetrievalFederated Learning	CodeCode Available	1	5
Enhancing Efficiency in Multidevice Federated Learning through Data Selection	Nov 8, 2022	Federated Learning	CodeCode Available	1	5
CENSOR: Defense Against Gradient Inversion via Orthogonal Subspace Bayesian Sampling	Jan 27, 2025	Federated Learning	CodeCode Available	1	5
Dual-Personalizing Adapter for Federated Foundation Models	Mar 28, 2024	Federated Learning	CodeCode Available	1	5
FedCorr: Multi-Stage Federated Learning for Label Noise Correction	Apr 10, 2022	Federated LearningPrivacy Preserving	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
FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated Learning	Aug 21, 2023	Federated LearningKnowledge Distillation	CodeCode Available	1	5
Dynamic Defense Against Byzantine Poisoning Attacks in Federated Learning	Jul 29, 2020	Data PoisoningFederated 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