FLINT: Fingerprinting Federated Learning Architectures from 5G PHY-Layer Side Channels
Researchers present FLINT, a black-box framework capable of inferring federated learning model architecture families (such as CNNs, RNNs, and Transformers) by analyzing only 5G physical-layer side-channel information. FLINT operates without access to packet-level data, instead leveraging scheduling metadata from the 5G Physical Downlink Control Channel (PDCCH) to identify temporal patterns linked to specific model architectures. In over-the-air experiments, FLINT achieves a macro F1-score of 0.930 for architecture-family classification, demonstrating a new class of side-channel leakage in federated learning over 5G networks.
Why it matters: This work reveals a previously unrecognized security vulnerability in federated learning over 5G, showing that model architectures can be fingerprinted via physical-layer side channels, potentially enabling targeted attacks.
Full story at: arXiv AI/ML ↗