FedDC: Efficient protection scheme based on chaotic system in federated learning

Federated Learning (FL) enables collaborative model training across decentralized clients while keeping raw data local. However, existing privacy-preserving mechanisms, such as secure aggregation and differential privacy (DP), often introduce significant computational overhead or degrade model utility. To address this challenge, we propose FedDC , a lightweight FL framework that combines DP with chaos-based parameter scrambling. Unlike existing approaches that uniformly protect entire model updates, FedDC introduces selective and layer-aware protection for sensitive neural network parameters, enabling flexible privacy protection with minimal overhead. Extensive experiments on four image and text datasets show that FedDC effectively mitigates privacy leakage under both black-box and white-box attacks while maintaining competitive model accuracy and negligible computational overhead.