Method Drift›Parameter-efficient fine-tuning (LoRA family)
FedSA
Parameter-efficient fine-tuning (LoRA family)
superseded — cited as a baseline and beaten by newer methods
0 papers critique it · 3 beat it on benchmarks
Beaten on benchmarks
Head-to-head results where a newer method reports beating FedSA. Values are copied from the source paper's tables — verify against the cited paper.
- Shift-Dependent Asymmetry: Orthogonal Inverse Low-Rank Adaptation for Federated Medical Segmentation
Shift-Dependent Asymmetry beats FedSA · Avg [Histology nuclei, LoRA Rank=8]
81.40 vs 80.09
- Shift-Dependent Asymmetry: Orthogonal Inverse Low-Rank Adaptation for Federated Medical Segmentation
Shift-Dependent Asymmetry beats FedSA · Avg [Fundus photography images, LoRA Rank=8]
84.52 vs 83.04
- Shift-Dependent Asymmetry: Orthogonal Inverse Low-Rank Adaptation for Federated Medical Segmentation
Shift-Dependent Asymmetry beats FedSA · Avg [Histology nuclei, LoRA Rank=16]
81.49 vs 80.61
- Shift-Dependent Asymmetry: Orthogonal Inverse Low-Rank Adaptation for Federated Medical Segmentation
Shift-Dependent Asymmetry beats FedSA · Avg [Fundus photography images, LoRA Rank=16]
85.70 vs 84.68
- FedALT: Federated Fine-Tuning through Adaptive Local Training with Rest-of-World LoRA
FedALT beats FedSA · Average [LLaMA2-7B]
67.55 vs 63.47
- FedALT: Federated Fine-Tuning through Adaptive Local Training with Rest-of-World LoRA
FedALT beats FedSA · Average [Bloom-560M]
48.09 vs 46.98
- Personalized Federated Fine-Tuning of Vision Foundation Models for Healthcare
FedOPAL-W beats FedSA · Balanced client test accuracy [Camelyon17-WILDS task]
0.850 vs 0.844
- Personalized Federated Fine-Tuning of Vision Foundation Models for Healthcare
FedOPAL-R beats FedSA · Balanced client test accuracy [Camelyon17-WILDS task]
0.870 vs 0.844
Newer alternatives
Recent methods in the same sub-problem, not yet superseded in the knowledge base.
- Shift-Dependent AsymmetryShift-Dependent Asymmetry: Orthogonal Inverse Low-Rank Adaptation for Federated Medical SegmentationJun 7, 2026
- May 7, 2026
- Mar 3, 2026
- Feb 27, 2026
- Nov 23, 2025
- compute-efficient continual pre-training with LoRALow-Resource Dialect Adaptation of Large Language Models: A French Dialect Case-StudyOct 26, 2025
- personalized federated fine-tuning method with orthogonal LoRA adaptersPersonalized Federated Fine-Tuning of Vision Foundation Models for HealthcareOct 14, 2025
- FLoRA-NACommunication-Efficient and Accurate Approach for Aggregation in Federated Low-Rank AdaptationSep 30, 2025