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 4376–4400 of 6771 papers

Title	Date	Tasks	Status
FedQUIT: On-Device Federated Unlearning via a Quasi-Competent Virtual Teacher	Aug 14, 2024	Federated LearningKnowledge Distillation	—Unverified
FedRAD: Federated Robust Adaptive Distillation	Dec 2, 2021	Federated LearningKnowledge Distillation	—Unverified
FedRand: Enhancing Privacy in Federated Learning with Randomized LoRA Subparameter Updates	Mar 10, 2025	Federated Learning	—Unverified
FedRBE -- a decentralized privacy-preserving federated batch effect correction tool for omics data based on limma	Dec 8, 2024	Federated LearningMissing Values	—Unverified
FedRDF: A Robust and Dynamic Aggregation Function against Poisoning Attacks in Federated Learning	Feb 15, 2024	Federated LearningModel Poisoning	—Unverified
FedRDMA: Communication-Efficient Cross-Silo Federated LLM via Chunked RDMA Transmission	Mar 1, 2024	Federated Learning	—Unverified
FedRec+: Enhancing Privacy and Addressing Heterogeneity in Federated Recommendation Systems	Oct 31, 2023	Federated LearningRecommendation Systems	—Unverified
FedRecon: Missing Modality Reconstruction in Distributed Heterogeneous Environments	Apr 14, 2025	Federated LearningImputation	—Unverified
FedRecover: Recovering from Poisoning Attacks in Federated Learning using Historical Information	Oct 20, 2022	Federated Learning	—Unverified
An Adaptive Federated Relevance Framework for Spatial Temporal Graph Learning	Jun 7, 2022	Federated LearningGraph Learning	—Unverified
FedREP: A Byzantine-Robust, Communication-Efficient and Privacy-Preserving Framework for Federated Learning	Mar 9, 2023	Federated LearningPrivacy Preserving	—Unverified
FedRepOpt: Gradient Re-parametrized Optimizers in Federated Learning	Sep 24, 2024	Federated LearningPrivacy Preserving	—Unverified
FedREP: Towards Horizontal Federated Load Forecasting for Retail Energy Providers	Mar 1, 2022	Federated LearningLoad Forecasting	—Unverified
FedRE: Robust and Effective Federated Learning with Privacy Preference	May 8, 2025	Federated Learning	—Unverified
FedReview: A Review Mechanism for Rejecting Poisoned Updates in Federated Learning	Feb 26, 2024	Federated Learning	—Unverified
FedRewind: Rewinding Continual Model Exchange for Decentralized Federated Learning	Nov 14, 2024	Continual LearningFederated Learning	—Unverified
FedRFQ: Prototype-Based Federated Learning with Reduced Redundancy, Minimal Failure, and Enhanced Quality	Jan 15, 2024	Federated Learning	—Unverified
FedRight: An Effective Model Copyright Protection for Federated Learning	Mar 18, 2023	Federated Learning	—Unverified
FedRobo: Federated Learning Driven Autonomous Inter Robots Communication For Optimal Chemical Sprays	Aug 10, 2024	Federated Learning	—Unverified
FedRPCA: Enhancing Federated LoRA Aggregation Using Robust PCA	Jun 1, 2025	Federated LearningTask Arithmetic	—Unverified
FedRS-Bench: Realistic Federated Learning Datasets and Benchmarks in Remote Sensing	May 13, 2025	Federated Learning	—Unverified
FedRSClip: Federated Learning for Remote Sensing Scene Classification Using Vision-Language Models	Jan 5, 2025	Federated Learningimage-classification	—Unverified
FedSA: Accelerating Intrusion Detection in Collaborative Environments with Federated Simulated Annealing	May 23, 2022	Federated LearningIntrusion Detection	—Unverified
FedSA: A Unified Representation Learning via Semantic Anchors for Prototype-based Federated Learning	Jan 9, 2025	Classifier calibrationContrastive Learning	—Unverified
FedSAE: A Novel Self-Adaptive Federated Learning Framework in Heterogeneous Systems	Apr 15, 2021	Active 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