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Federated Learning (FL) enables IoT devices to collaboratively train models without sharing raw data. However, privacy and accountability remain major challenges due to persistent identifiers and untrusted participants. We propose a lightweight FL framework integrating anonymous credentials and local differential privacy for unlinkable participation and secure model updates. A zk-SNARK-based revocation mechanism enables efficient verification and blacklisting of malicious clients. Experiments confirm low computational overhead and strong protection against inference and poisoning attacks.

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Enhancing Privacy and Accountability in Federated Learning for Smart IoT

  • 会议论文

  • 发表日期：

    2026年

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  • Zhuotao Lian,
  • Qingkui Zeng,
  • Chunhua Su

摘要

Federated Learning (FL) enables IoT devices to collaboratively train models without sharing raw data. However, privacy and accountability remain major challenges due to persistent identifiers and untrusted participants. We propose a lightweight FL framework integrating anonymous credentials and local differential privacy for unlinkable participation and secure model updates. A zk-SNARK-based revocation mechanism enables efficient verification and blacklisting of malicious clients. Experiments confirm low computational overhead and strong protection against inference and poisoning attacks.