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

• URL: http://arxiv.org/abs/2603.20615v1
• Date: Sat, 21 Mar 2026 03:19:05 GMT
• Title: Unveiling the Security Risks of Federated Learning in the Wild: From Research to Practice
• Abstract summary: Federated learning (FL) has attracted substantial attention in both academia and industry.<n>But its practical security posture remains poorly understood.<n>This paper revisits FL security from a measurement perspective.
• Score: 50.84321144889696
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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

Related papers

• Anchoring Refusal Direction: Mitigating Safety Risks in Tuning via Projection Constraint [52.878820730054365]
  Instruction Fine-Tuning (IFT) has been widely adopted as an effective post-training strategy to enhance abilities of Large Language Models (LLMs)<n>Recent research into the internal mechanisms of LLMs has identified the refusal direction (r-direction) in the hidden states, which plays a pivotal role in governing refusal behavior.<n>To mitigate such drift, our proposed ProCon method introduces a projection-constrained loss term that regularizes the projection magnitude of each training sample's hidden state onto the r-direction.
  arXiv  Detail & Related papers  (2025-09-08T15:24:33Z)
• ROSE: Toward Reality-Oriented Safety Evaluation of Large Language Models [60.28667314609623]
  Large Language Models (LLMs) are increasingly deployed as black-box components in real-world applications.<n>We propose Reality-Oriented Safety Evaluation (ROSE), a novel framework that uses multi-objective reinforcement learning to fine-tune an adversarial LLM.
  arXiv  Detail & Related papers  (2025-06-17T10:55:17Z)
• Feasibility-Aware Pessimistic Estimation: Toward Long-Horizon Safety in Offline RL [14.767273209148545]
  We propose a novel framework Feasibility-Aware offline Safe Reinforcement Learning with CVAE-based Pessimism (FASP)<n>We employ Hamilton-Jacobi (H-J) reachability analysis to generate reliable safety labels.<n>We also utilize pessimistic estimation methods to estimate the Q-value of reward and cost.
  arXiv  Detail & Related papers  (2025-05-13T02:32:49Z)
• Sparsification Under Siege: Defending Against Poisoning Attacks in Communication-Efficient Federated Learning [9.911465831373423]
  Federated Learning (FL) enables collaborative model training across distributed clients while preserving data privacy.<n>It faces significant challenges in communication efficiency and vulnerability to poisoning attacks.<n>We propose FLARE, a novel federated learning framework that integrates sparse index mask inspection and model update sign similarity analysis.
  arXiv  Detail & Related papers  (2025-04-30T14:59:13Z)
• Celtibero: Robust Layered Aggregation for Federated Learning [0.0]
  We introduce Celtibero, a novel defense mechanism that integrates layered aggregation to enhance robustness against adversarial manipulation. We demonstrate that Celtibero consistently achieves high main task accuracy (MTA) while maintaining minimal attack success rates (ASR) across a range of untargeted and targeted poisoning attacks.
  arXiv  Detail & Related papers  (2024-08-26T12:54:00Z)
• SPFL: A Self-purified Federated Learning Method Against Poisoning Attacks [12.580891810557482]
  Federated learning (FL) is attractive for pulling privacy-preserving distributed training data. We propose a self-purified FL (SPFL) method that enables benign clients to exploit trusted historical features of locally purified model. We experimentally demonstrate that SPFL outperforms state-of-the-art FL defenses against various poisoning attacks.
  arXiv  Detail & Related papers  (2023-09-19T13:31:33Z)
• Safety Margins for Reinforcement Learning [53.10194953873209]
  We show how to leverage proxy criticality metrics to generate safety margins. We evaluate our approach on learned policies from APE-X and A3C within an Atari environment.
  arXiv  Detail & Related papers  (2023-07-25T16:49:54Z)
• FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning [66.56240101249803]
  We study how hardening benign clients can affect the global model (and the malicious clients) We propose a trigger reverse engineering based defense and show that our method can achieve improvement with guarantee robustness. Our results on eight competing SOTA defense methods show the empirical superiority of our method on both single-shot and continuous FL backdoor attacks.
  arXiv  Detail & Related papers  (2022-10-23T22:24:03Z)
• Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Federated Learning [32.88150721857589]
  Recent works have indicated that federated learning (FL) is vulnerable to poisoning attacks by compromised clients. We show that these works make a number of unrealistic assumptions and arrive at somewhat misleading conclusions. We perform the first critical analysis of poisoning attacks under practical production FL environments.
  arXiv  Detail & Related papers  (2021-08-23T15:29:45Z)
• Conservative Safety Critics for Exploration [120.73241848565449]
  We study the problem of safe exploration in reinforcement learning (RL) We learn a conservative safety estimate of environment states through a critic. We show that the proposed approach can achieve competitive task performance while incurring significantly lower catastrophic failure rates.
  arXiv  Detail & Related papers  (2020-10-27T17:54:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.