FedMID: A Data-Free Method for Using Intermediate Outputs as a Defense Mechanism Against Poisoning Attacks in Federated Learning
<|reference_start|>FedMID: A Data-Free Method for Using Intermediate Outputs as a Defense Mechanism Against Poisoning Attacks in Federated Learning: Federated learning combines local updates from clients to produce a global model, which is susceptible to poisoning attacks. Most previous defense strategies relied on vectors derived from projections of local updates on a Euclidean space; however, these methods fail to accurately represent the functionality and structure of local models, resulting in inconsistent performance. Here, we present a new paradigm to defend against poisoning attacks in federated learning using functional mappings of local models based on intermediate outputs. Experiments show that our mechanism is robust under a broad range of computing conditions and advanced attack scenarios, enabling safer collaboration among data-sensitive participants via federated learning.<|reference_end|>
@article{han2024fedmid:,
    title={FedMID: A Data-Free Method for Using Intermediate Outputs as a Defense
  Mechanism Against Poisoning Attacks in Federated Learning},
    author={Sungwon Han, Hyeonho Song, Sungwon Park, Meeyoung Cha},
    journal={arXiv preprint arXiv:2404.11905},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.11905},
    primaryClass={cs.LG cs.CR}
}