Byzantine-Robust Asynchronous Federated Learning via Feature Fingerprinting

Summary

Asynchronous federated learning is more exposed to Byzantine attacks, and existing defenses fail under data and device heterogeneity. The authors propose Belisa, which compares feature representations of local models against a reference model trained on public data, then clusters to filter out malicious models. Across six datasets, three task types and five Byzantine attacks, Belisa lowers the average test error under attack to 0.42 times that of baselines and speeds aggregation by an average of 12.3 times.

Related items

• CriticalCVE-2026-48705: MONAI PydicomReader affine computation accepts non-finite DICOM valuesSimilar attack · NVD/CVE Database
• InfoSigned but Steerable: Measuring and Closing the Text-Vector Integrity Gap in Offline Retrieval-Augmented GenerationSimilar attack · Arxiv (cs.CR + cs.CL + cs.LG)
• InfoSecuring next-generation edge intelligence: A survey from federated learning to autonomous agents in cyber-physical systemsSimilar attack · OpenAlex (peer-reviewed AI security)
• InfoPop Goes the Verifier: Phoneme-Targeted Data Poisoning of Voice Liveness DetectionSimilar attack · Arxiv (cs.RO + cs.CV security)
• InfoCharacterizing Recoverable Expert-Utilization Signatures in Fine-Tuned Mixture-of-Experts LLMsSimilar attack · Arxiv (cs.CR + cs.CL + cs.LG)