LOGSAFE: Logic-Guided Verification for Trustworthy Federated Time-Series Learning
Abstract
This paper introduces LOGSAFE, a defense mechanism for federated learning in time series settings, particularly within cyber-physical systems. It addresses poisoning attacks by moving beyond traditional update-similarity methods and instead using logical reasoning to evaluate client reliability. LOGSAFE extracts client-specific temporal properties, infers global patterns, and verifies clients against them to detect and exclude malicious participants. Experiments show that it significantly outperforms existing methods, achieving up to 93.27% error reduction over the next best baseline. Our code is available at https://github.com/judydnguyen/LOGSAFE-Robust-FTS.
Fields of Study & Subjects
Internet Traffic Analysis and Secure E-voting
Network Security and Intrusion Detection
Publication details
- Journal / source
- 2026 ACM/IEEE 17th International Conference on Cyber-Physical Systems (ICCPS)
- Publisher
- IEEE
- Pages
- 118–129
- Publication date
- 11 May 2026
- Language
- EN