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 301–325 of 6771 papers

Title	Date	Tasks	Status	Hype
FRAIN to Train: A Fast-and-Reliable Solution for Decentralized Federated Learning	May 7, 2025	Federated Learningimage-classification	CodeCode Available	0
FedBWO: Enhancing Communication Efficiency in Federated Learning	May 7, 2025	Federated Learning	—Unverified	0
Cluster-Aware Multi-Round Update for Wireless Federated Learning in Heterogeneous Environments	May 6, 2025	Federated Learning	—Unverified	0
Small-Scale-Fading-Aware Resource Allocation in Wireless Federated Learning	May 6, 2025	Federated LearningMulti-agent Reinforcement Learning	—Unverified	0
Bayesian Robust Aggregation for Federated Learning	May 5, 2025	Bayesian InferenceFederated Learning	CodeCode Available	0
Towards One-shot Federated Learning: Advances, Challenges, and Future Directions	May 5, 2025	Federated LearningSurvey	CodeCode Available	4
HSplitLoRA: A Heterogeneous Split Parameter-Efficient Fine-Tuning Framework for Large Language Models	May 5, 2025	Federated Learningparameter-efficient fine-tuning	—Unverified	0
Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data	May 5, 2025	Federated LearningInfluence Approximation	—Unverified	0
Incentivizing Inclusive Contributions in Model Sharing Markets	May 5, 2025	Federated LearningInstruction Following	—Unverified	0
Bayesian Federated Cause-of-Death Classification and Quantification Under Distribution Shift	May 4, 2025	Federated Learning	—Unverified	0
Mitigating Group-Level Fairness Disparities in Federated Visual Language Models	May 3, 2025	counterfactualFairness	—Unverified	0
Privacy Preserving Machine Learning Model Personalization through Federated Personalized Learning	May 3, 2025	Federated LearningPrivacy Preserving	—Unverified	0
Towards Trustworthy Federated Learning with Untrusted Participants	May 3, 2025	Federated Learning	—Unverified	0
PQS-BFL: A Post-Quantum Secure Blockchain-based Federated Learning Framework	May 3, 2025	Federated Learning	—Unverified	0
Federated Adapter on Foundation Models: An Out-Of-Distribution Approach	May 2, 2025	Federated Learningparameter-efficient fine-tuning	—Unverified	0
Secure Cluster-Based Hierarchical Federated Learning in Vehicular Networks	May 2, 2025	Anomaly DetectionFederated Learning	—Unverified	0
AI-Driven IRM: Transforming insider risk management with adaptive scoring and LLM-based threat detection	May 1, 2025	Anomaly DetectionFederated Learning	—Unverified	0
Mitigating Adversarial Attacks on ECG Classification in Federated Learning via Adversarial Training	May 1, 2025	ECG ClassificationElectrocardiography (ECG)	CodeCode Available	0
Communication-Efficient Wireless Federated Fine-Tuning for Large-Scale AI Models	May 1, 2025	Federated Learning	—Unverified	0
FedEMA: Federated Exponential Moving Averaging with Negative Entropy Regularizer in Autonomous Driving	May 1, 2025	Autonomous DrivingFederated Learning	—Unverified	0
Open-Source LLM-Driven Federated Transformer for Predictive IoV Management	May 1, 2025	Federated LearningManagement	—Unverified	0
A Generalized Meta Federated Learning Framework with Theoretical Convergence Guarantees	Apr 30, 2025	Federated Learning	—Unverified	0
Online Federation For Mixtures of Proprietary Agents with Black-Box Encoders	Apr 30, 2025	AI AgentFederated Learning	—Unverified	0
Sparsification Under Siege: Defending Against Poisoning Attacks in Communication-Efficient Federated Learning	Apr 30, 2025	Federated Learning	—Unverified	0
Whispers of Data: Unveiling Label Distributions in Federated Learning Through Virtual Client Simulation	Apr 30, 2025	Federated LearningInference Attack	—Unverified	0

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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