A single adversary in distributed training inflates its attribution value via latent optimization on synthetic batches without degrading accuracy or triggering basic defenses.
Threats tofederated learning: A survey
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FLARE uses adaptive multi-dimensional reputation scores and soft exclusion to improve Byzantine robustness in federated learning by up to 16% over prior methods while handling a new Statistical Mimicry attack.
A three-stage pill-based augmentation makes existing FL poisoning attacks evade popular defenses while raising error rates up to 7x on both IID and non-IID data.
GPP trains local variational encoders in federated settings to release representations that keep utility within 1% of an autoencoder baseline while driving adversary AUC on sensitive attributes to near-random levels on MNIST, CelebA, and HAPT data.
The paper introduces clean-model-based metrics that stratify test samples by vulnerability to targeted poisoning, enabling worst-case attack evaluation and vulnerability-aware defenses.
The survey introduces personalized federated intelligence (PFI) as a framework integrating federated learning and foundation models to support privacy-aware personalization of AI models.
citing papers explorer
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On the Fragility of Data Attribution When Learning Is Distributed
A single adversary in distributed training inflates its attribution value via latent optimization on synthetic batches without degrading accuracy or triggering basic defenses.
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FLARE: Adaptive Multi-Dimensional Reputation for Robust Client Reliability in Federated Learning
FLARE uses adaptive multi-dimensional reputation scores and soft exclusion to improve Byzantine robustness in federated learning by up to 16% over prior methods while handling a new Statistical Mimicry attack.
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Poisoning with A Pill: Circumventing Detection in Federated Learning
A three-stage pill-based augmentation makes existing FL poisoning attacks evade popular defenses while raising error rates up to 7x on both IID and non-IID data.
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Distributed Deep Variational Approach for Privacy-preserving Data Release
GPP trains local variational encoders in federated settings to release representations that keep utility within 1% of an autoencoder baseline while driving adversary AUC on sensitive attributes to near-random levels on MNIST, CelebA, and HAPT data.
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Are Targeted Data Poisoning Attacks as Effective as We Think?
The paper introduces clean-model-based metrics that stratify test samples by vulnerability to targeted poisoning, enabling worst-case attack evaluation and vulnerability-aware defenses.
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A Survey on Foundation Models for Personalized Federated Intelligence
The survey introduces personalized federated intelligence (PFI) as a framework integrating federated learning and foundation models to support privacy-aware personalization of AI models.