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Threats to Federated Learning: A Survey
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With the emergence of data silos and popular privacy awareness, the traditional centralized approach of training artificial intelligence (AI) models is facing strong challenges. Federated learning (FL) has recently emerged as a promising solution under this new reality. Existing FL protocol design has been shown to exhibit vulnerabilities which can be exploited by adversaries both within and without the system to compromise data privacy. It is thus of paramount importance to make FL system designers to be aware of the implications of future FL algorithm design on privacy-preservation. Currently, there is no survey on this topic. In this paper, we bridge this important gap in FL literature. By providing a concise introduction to the concept of FL, and a unique taxonomy covering threat models and two major attacks on FL: 1) poisoning attacks and 2) inference attacks, this paper provides an accessible review of this important topic. We highlight the intuitions, key techniques as well as fundamental assumptions adopted by various attacks, and discuss promising future research directions towards more robust privacy preservation in FL.
Forward citations
Cited by 7 Pith papers
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FedThief: Harming Others to Benefit Oneself in Self-Centered Federated Learning
FedThief lets malicious federated learning clients poison the global model while training a private ensemble model that outperforms it.
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AnalogFed: Privacy-Preserving Discovery of Analog Circuits at Scale with Federated Generative AI
AnalogFed combines federated learning with a generative analog-topology model, adding dummy-token input perturbation and partial homomorphic encryption to resist membership inference and model inversion attacks.
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Integrity of peer-to-peer distributed LLM inference under malicious nodes
Under a simulated isotropic noise model, a canary-trap activation-drift detector achieves perfect AUROC separation of one malicious shard in multi-hop LLM inference.
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SoK: Federated Learning for Intrusion Detection in Vehicular Networks
Auditing over 60 vehicular FL-IDS papers reveals pervasive evaluation pitfalls (IID splits, trivial datasets, missing Byzantine and real-time checks) and proposes minimum benchmarking requirements plus a research agenda.
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FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks
FLAegis defends federated learning by SAX-transforming client updates, spectral-clustering them to filter malicious clients, and applying FFT-based robust aggregation, outperforming several baselines on FEMNIST.
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A Bayesian Incentive Mechanism for Poison-Resilient Federated Learning
A validation-loss threshold with per-round payments keeps MNIST accuracy at 96.7% under 50% label-flipping adversaries, but the incentive-compatibility claim is a restatement of the payment rule.
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PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction
A federated, privacy-preserving RDSN framework for encrypted image reconstruction whose local differential privacy mechanism is not actually differentially private because it releases low-frequency DCT coefficients wi...
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