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 1–25 of 6771 papers

Title	Date	Tasks	Status	Hype	Score
Towards One-shot Federated Learning: Advances, Challenges, and Future Directions	May 5, 2025	Federated LearningSurvey	CodeCode Available	4	5
PFLlib: A Beginner-Friendly and Comprehensive Personalized Federated Learning Library and Benchmark	Dec 8, 2023	Federated LearningPersonalized Federated Learning	CodeCode Available	4	5
FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policy	Jul 1, 2023	Federated LearningPersonalized Federated Learning	CodeCode Available	4	5
Differential Privacy: What is all the noise about?	May 19, 2022	AllFederated Learning	CodeCode Available	4	5
FedML Parrot: A Scalable Federated Learning System via Heterogeneity-aware Scheduling on Sequential and Hierarchical Training	Mar 3, 2023	Federated LearningGPU	CodeCode Available	4	5
FLEX: FLEXible Federated Learning Framework	Apr 9, 2024	Federated Learning	CodeCode Available	4	5
FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System	Mar 20, 2023	Federated LearningPrivacy Preserving	CodeCode Available	4	5
GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated Learning	Aug 20, 2023	FairnessFederated Learning	CodeCode Available	4	5
Eliminating Domain Bias for Federated Learning in Representation Space	Nov 25, 2023	Federated LearningPrivacy Preserving	CodeCode Available	4	5
A Survey on LoRA of Large Language Models	Jul 8, 2024	Federated Learningparameter-efficient fine-tuning	CodeCode Available	3	5
pfl-research: simulation framework for accelerating research in Private Federated Learning	Apr 9, 2024	Federated Learning	CodeCode Available	3	5
OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning	Feb 10, 2024	Federated LearningInstruction Following	CodeCode Available	3	5
Improved Modelling of Federated Datasets using Mixtures-of-Dirichlet-Multinomials	Jun 4, 2024	Federated Learning	CodeCode Available	3	5
FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language Models	Jun 7, 2024	Federated Learning	CodeCode Available	3	5
HtFLlib: A Comprehensive Heterogeneous Federated Learning Library and Benchmark	Jun 4, 2025	Federated LearningTransfer Learning	CodeCode Available	3	5
Calibre: Towards Fair and Accurate Personalized Federated Learning with Self-Supervised Learning	Dec 28, 2024	FairnessFederated Learning	CodeCode Available	3	5
Efficient Federated Learning Tiny Language Models for Mobile Network Feature Prediction	Apr 2, 2025	Federated Learning	CodeCode Available	2	5
Enhancing Privacy in Federated Learning: Secure Aggregation for Real-World Healthcare Applications	Sep 2, 2024	CPUFederated Learning	CodeCode Available	2	5
Cooperative Edge Caching Based on Elastic Federated and Multi-Agent Deep Reinforcement Learning in Next-Generation Network	Jan 18, 2024	Deep Reinforcement LearningFederated Learning	CodeCode Available	2	5
DPAUC: Differentially Private AUC Computation in Federated Learning	Aug 25, 2022	Federated Learning	CodeCode Available	2	5
FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models	Oct 16, 2023	Federated Learningparameter-efficient fine-tuning	CodeCode Available	2	5
COALA: A Practical and Vision-Centric Federated Learning Platform	Jul 23, 2024	BenchmarkingContinual Learning	CodeCode Available	2	5
Advances and Open Problems in Federated Learning	Dec 10, 2019	BIG-bench Machine LearningFederated Learning	CodeCode Available	2	5
Adaptive Personalized Federated Learning	Mar 30, 2020	Bilevel OptimizationFederated Learning	CodeCode Available	2	5
BackFed: An Efficient & Standardized Benchmark Suite for Backdoor Attacks in Federated Learning	Jul 7, 2025	Federated Learning	CodeCode Available	2	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