Unveiling the Security Risks of Federated Learning in the Wild: From Research to Practice

TL;DR

This paper critically evaluates federated learning security by comparing idealized research assumptions with practical deployment realities, revealing that many attack methods are less effective in real-world settings and emphasizing the need for realistic evaluation metrics.

Contribution

It introduces TFLlib, a comprehensive evaluation framework that assesses poisoning attacks under practical conditions, highlighting discrepancies between research claims and real-world security risks.

Findings

Idealized attack success overstates real-world risks

Attack effectiveness varies with dataset and stability constraints

Practical metrics should include effectiveness, stability, and utility loss

Abstract

Federated learning (FL) has attracted substantial attention in both academia and industry, yet its practical security posture remains poorly understood. In particular, a large body of poisoning research is evaluated under idealized assumptions about attacker participation, client homogeneity, and success metrics, which can substantially distort how security risks are perceived in deployed FL systems. This paper revisits FL security from a measurement perspective. We systematize three major sources of mismatch between research and practice: unrealistic poisoning threat models, the omission of hybrid heterogeneity, and incomplete metrics that overemphasize peak attack success while ignoring stability and utility cost. To study these gaps, we build TFLlib, a uniform evaluation framework that supports image, text, and tabular FL tasks and re-implements representative poisoning attacks under practical settings. Our empirical study shows that idealized evaluation often overstates security risk. Under practical settings, attack performance becomes markedly more dataset-dependent and unstable, and several attacks that appear consistently strong in idealized FL lose effectiveness or incur clear benign-task degradation once practical constraints are enforced. These findings further show that final-round attack success alone is insufficient for security assessment; practical measurement must jointly consider effectiveness, temporal stability, and collateral utility loss. Overall, this work argues that many conclusions in the FL poisoning literature are not directly transferable to real deployments. By tightening the threat model and using measurement protocols aligned with practice, we provide a more realistic view of the security risks faced by contemporary FL systems and distill concrete guidance for future FL security evaluation. Our code is available at https://github.com/xaddwell/TFLlib