← Back to all news
arXiv AI By Kichang Lee, Jaeho Jin, JaeYeon Park, Songkuk Kim, JeongGil Ko

Temperature Scaling Attack Disrupting Model Confidence in Federated Learning

Read the original on arXiv AI →

The paper introduces the Temperature Scaling Attack (TSA), a training‑time method that degrades model confidence calibration while keeping predictive accuracy largely intact. TSA injects temperature scaling with a learning‑rate coupling during local federated training, shifting confidence scores and causing significant calibration errors (e.g., a 145% increase on CIFAR‑100) with less than a 2% drop in accuracy. The authors provide a convergence analysis for non‑IID settings and demonstrate TSA’s effectiveness across three benchmarks, robust aggregation, and post‑hoc calibration defenses, highlighting its impact on mission‑critical systems such as healthcare verification and autonomous driving.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

Hugging Face Trending Papers
Aug 4

Robust and Personalized Federated Learning for Aircraft-Engine Prognostics under Benign and Adversarial Client Heterogeneity

Federated learning (FL) enables aircraft fleet operators to jointly train remaining-useful-life (RUL) models from engine sensor telemetry without sharing raw data. This study examines two complementary challenges: benign heterogeneity, where honest operators observe different operating conditions and fault modes, and adversarial heterogeneity, where compromised operators submit poisoned updates.

benchmarkssafety
More like this →
arXiv Machine Learning
Jul 10

Securing Autonomous Vehicle Systems via Twin-Aware Federated Reinforcement Learning

arXiv:2607. 08137v1 Announce Type: cross Abstract: Federated reinforcement learning (FRL) is crucial for enabling collaborative learning across multiple agents without sharing raw data, thereby enhancing privacy and scalability in the decision-making process within dynamic vehicular environments.

By Zifan Zhang, Minghong Fang, Dianwei Chen, Zhuqing Liu, Prashant Khanduri, Xianfeng Yang, Anupam Das, Yuchen Liu
agentsreinforcement-learningroboticssafety
More like this →
arXiv AI
Sep 24

When Clients Are Orchestrated: Strategic Gradient Manipulation to Defeat Federated Learning Servers with Efficient Defense

The paper introduces Fed-ADR, a coordinated attack framework where a malicious orchestrator server directs heterogeneous adversarial clients to adapt their gradient updates in real time, thereby evading existing federated learning defenses and drastically reducing global model accuracy. It also presents a lightweight detection mechanism that estimates true client gradients from historical data to spot coordinated attacks, and an in-situ recovery method that restores model performance without restarting training. Experiments on MNIST, Fashion‑MNIST, and CIFAR‑10 show the attack can drop accuracy from over 90% to below 10%, while the defense can recover accuracy to above 90% within a few rounds at a computational cost at least 20× lower than retraining from scratch.

By Mohamed Shaaban, Ahmed Abdelnaby, Mohamed Elmahallawy
roboticsbenchmarkssafety
More like this →