FedSAF shifts prototype alignment in heterogeneous federated learning from coordinate matching to inter-class structural relations and reports up to 3.52% gains over prior methods.
Tiny imagenet visual recognition challenge,
6 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
verdicts
UNVERDICTED 6representative citing papers
Unlearnable examples fail under pretraining-finetuning due to semantic filtering by frozen layers, but Shallow Semantic Camouflage restores effectiveness by confining perturbations to semantically valid subspaces.
DFBScanner detects backdoors by combining anomaly indicators from final-layer parameters into a Trojan clue score, reporting 97.17% true-positive rate, 0.95% false-positive rate, and 1 ms average detection time on a benchmark of over 5,000 models.
ArmSSL is a black-box verifiable and adversarially robust watermarking framework for SSL pre-trained encoders using paired discrepancy enlargement, latent entanglement, distribution alignment, and reference-guided tuning.
BicKD introduces a bilateral contrastive loss in knowledge distillation that strengthens class-wise orthogonality and intra-class consistency in predictive distributions, outperforming prior logit-based methods.
Fed-TaLoRA uses task-agnostic low-rank residual adaptation with post-aggregation calibration to enable efficient federated continual fine-tuning across sequential tasks under non-IID conditions.
citing papers explorer
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From Coordinate Matching to Structural Alignment: Rethinking Prototype Alignment in Heterogeneous Federated Learning
FedSAF shifts prototype alignment in heterogeneous federated learning from coordinate matching to inter-class structural relations and reports up to 3.52% gains over prior methods.
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Channel-Level Semantic Perturbations: Unlearnable Examples for Diverse Training Paradigms
Unlearnable examples fail under pretraining-finetuning due to semantic filtering by frozen layers, but Shallow Semantic Camouflage restores effectiveness by confining perturbations to semantically valid subspaces.
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Lightweight and Fast Backdoor Model Detection
DFBScanner detects backdoors by combining anomaly indicators from final-layer parameters into a Trojan clue score, reporting 97.17% true-positive rate, 0.95% false-positive rate, and 1 ms average detection time on a benchmark of over 5,000 models.
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ArmSSL: Adversarial Robust Black-Box Watermarking for Self-Supervised Learning Pre-trained Encoders
ArmSSL is a black-box verifiable and adversarially robust watermarking framework for SSL pre-trained encoders using paired discrepancy enlargement, latent entanglement, distribution alignment, and reference-guided tuning.
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BicKD: Bilateral Contrastive Knowledge Distillation
BicKD introduces a bilateral contrastive loss in knowledge distillation that strengthens class-wise orthogonality and intra-class consistency in predictive distributions, outperforming prior logit-based methods.
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Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning
Fed-TaLoRA uses task-agnostic low-rank residual adaptation with post-aggregation calibration to enable efficient federated continual fine-tuning across sequential tasks under non-IID conditions.